Introduction

For one of my machine learning classes we had a project that consumed financial data. I have extended that project to use machine learning to see if an indicator, or predictor, can be found that identifies market tops that occur prior to recessions. Then I use the model to build a trading strategy and backtest it to see how it performs.

Get Economic and Financial Data

Acquiring the data consists of two steps. First the code pulls the data into zoo objects which are then collapsed into a single data frame (df.data). Features are extracted from these series and added to the df.data data frame.

Sample call to pull economic data

Data is pulled from several sources include FRED, yahoo, and Google. The code below shows an example that pulls in the consumer price index (CPI) from the FRED. I pull data using quantmod, Quandl, and some manual extractions stored in spreadsheets.

# Consumer Price Index for All Urban Consumers: All Items
if (bRefresh == TRUE) {
  getSymbols("CPIAUCSL", src = "FRED", auto.assign = TRUE)
}
## [1] "CPIAUCSL"
## Warning: ASDAX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: ^TNX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: CL=F contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: ^IRX contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: ^RLG contains missing values. Some functions will not work if objects
## contain missing values in the middle of the series. Consider using na.omit(),
## na.approx(), na.fill(), etc to remove or replace them.
## Warning: ^STOXX50E contains missing values. Some functions will not work if
## objects contain missing values in the middle of the series. Consider using
## na.omit(), na.approx(), na.fill(), etc to remove or replace them.

Load rig count data

The Baker Hughes rig count numbers

USDA data

Loading in farm data

## Warning in read_fun(path = enc2native(normalizePath(path)), sheet_i = sheet, :
## Expecting numeric in E3 / R3C5: got a date
## New names:
## * `` -> ...1
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * ...
## Warning: NAs introduced by coercion

Loading in Silverblatt’s S&P 500 spreadsheet starting with the quarterly data.

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...5
## * `` -> ...6
## * `` -> ...7

Now load in the estimates

## New names:
## * `` -> ...2
## * `` -> ...3
## * `` -> ...4
## * `` -> ...5
## * `` -> ...6
## * ...

Covid 19 Data

Get the Covid-19 data from JHU

## Rows: 585750 Columns: 15
## -- Column specification ------------------------------------------------------------------------------------------------
## Delimiter: ","
## chr  (8): province, country, type, iso2, iso3, combined_key, continent_name,...
## dbl  (6): lat, long, cases, uid, code3, population
## date (1): date
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
## Downloading GitHub repo RamiKrispin/coronavirus@master
##   
  
  
v  checking for file 'C:\Users\Rainy\AppData\Local\Temp\RtmpYnKxKG\remotes733033f44972\RamiKrispin-coronavirus-85e3ae8/DESCRIPTION'
## 
  
  
  
-  preparing 'coronavirus': (1.3s)
##    checking DESCRIPTION meta-information ...
  
   checking DESCRIPTION meta-information ... 
  
v  checking DESCRIPTION meta-information
## 
  
  
  
-  checking for LF line-endings in source and make files and shell scripts
## 
  
  
  
-  checking for empty or unneeded directories
## 
  
  
  
-  building 'coronavirus_0.3.32.tar.gz'
## 
  
   
## 
## Caught an warning!
## <simpleWarning: package 'coronavirus' is in use and will not be installed>
## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.

## Warning: Removed 3 row(s) containing missing values (geom_path).

Feature Extraction

With the raw data downloaded, some of the interesting features can be extracted. The first step is reconcile the time intervals. Some of the data is released monthly and some daily. I chose to interpolate all data to a daily interval. The first section of code adds the daily rows to the dataframe.

The code performs interpolation for continuous data or carries it forward for binary data like the recession indicators.

source("calcInterpolate.r")
df.data <- calcInterpolate(df.data, df.symbols)
## Warning in merge.xts(xtsData, get(df.symbols$string.symbol[idx])): NAs
## introduced by coercion

Truncate data

Create aggregate series

Some analysis requires that two or more series be combined. For example, normallizing debt by GDP to get a sense of the proportion of debt to the total economy helps understand the debt cycle.

Year over year, smoothed derivative, and log trends tend to smooth out seasonal variation. It gets used so often that I do this for every series downloaded.

source("calcFeatures.r")
lst.df <- calcFeatures(df.data, df.symbols)
## [1] "USREC has zero or negative values. Log series will be zero."
## [1] "GSFTX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMIX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMCX.Volume has zero or negative values. Log series will be zero."
## [1] "LFMAX.Volume has zero or negative values. Log series will be zero."
## [1] "LCSIX.Volume has zero or negative values. Log series will be zero."
## [1] "VBIRX.Volume has zero or negative values. Log series will be zero."
## [1] "VFSUX.Volume has zero or negative values. Log series will be zero."
## [1] "LTUIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTPX.Volume has zero or negative values. Log series will be zero."
## [1] "NERYX.Volume has zero or negative values. Log series will be zero."
## [1] "STIGX.Volume has zero or negative values. Log series will be zero."
## [1] "HLGAX.Volume has zero or negative values. Log series will be zero."
## [1] "FTRGX.Volume has zero or negative values. Log series will be zero."
## [1] "THIIX.Volume has zero or negative values. Log series will be zero."
## [1] "PTTRX.Volume has zero or negative values. Log series will be zero."
## [1] "BFIGX.Volume has zero or negative values. Log series will be zero."
## [1] "EIFAX.Volume has zero or negative values. Log series will be zero."
## [1] "ASDAX.Volume has zero or negative values. Log series will be zero."
## [1] "TRBUX.Volume has zero or negative values. Log series will be zero."
## [1] "PRWCX.Volume has zero or negative values. Log series will be zero."
## [1] "ADOZX.Volume has zero or negative values. Log series will be zero."
## [1] "MERFX.Volume has zero or negative values. Log series will be zero."
## [1] "CMNIX.Volume has zero or negative values. Log series will be zero."
## [1] "CIHEX.Volume has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB has zero or negative values. Log series will be zero."
## [1] "TNX.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Open has zero or negative values. Log series will be zero."
## [1] "CLF.Low has zero or negative values. Log series will be zero."
## [1] "CLF.Close has zero or negative values. Log series will be zero."
## [1] "CLF.Volume has zero or negative values. Log series will be zero."
## [1] "CLF.Adjusted has zero or negative values. Log series will be zero."
## [1] "DTB3 has zero or negative values. Log series will be zero."
## [1] "IRX.Open has zero or negative values. Log series will be zero."
## [1] "IRX.High has zero or negative values. Log series will be zero."
## [1] "IRX.Low has zero or negative values. Log series will be zero."
## [1] "IRX.Close has zero or negative values. Log series will be zero."
## [1] "IRX.Volume has zero or negative values. Log series will be zero."
## [1] "IRX.Adjusted has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO has zero or negative values. Log series will be zero."
## [1] "RLG.Volume has zero or negative values. Log series will be zero."
## [1] "STOXX50E.Volume has zero or negative values. Log series will be zero."
## [1] "GDPNOW has zero or negative values. Log series will be zero."
## [1] "W790RC1Q027SBEA has zero or negative values. Log series will be zero."
## [1] "VXX.Volume has zero or negative values. Log series will be zero."
## [1] "FYFSD has zero or negative values. Log series will be zero."
## [1] "FYFSGDA188S has zero or negative values. Log series will be zero."
## [1] "SOFR25 has zero or negative values. Log series will be zero."
## [1] "SOFR1 has zero or negative values. Log series will be zero."
## [1] "RPONTSYD has zero or negative values. Log series will be zero."
## [1] "BOPGTB has zero or negative values. Log series will be zero."
## [1] "EES.Volume has zero or negative values. Log series will be zero."
## [1] "VGSTX.Volume has zero or negative values. Log series will be zero."
## [1] "VFINX.Volume has zero or negative values. Log series will be zero."
## [1] "TMFGX.Volume has zero or negative values. Log series will be zero."
## [1] "HAINX.Volume has zero or negative values. Log series will be zero."
## [1] "IVOO.Volume has zero or negative values. Log series will be zero."
## [1] "VO.Volume has zero or negative values. Log series will be zero."
## [1] "CZA.Volume has zero or negative values. Log series will be zero."
## [1] "SLY.Volume has zero or negative values. Log series will be zero."
## [1] "HYMB.Volume has zero or negative values. Log series will be zero."
## [1] "GOLD.Open has zero or negative values. Log series will be zero."
## [1] "GOLD.Volume has zero or negative values. Log series will be zero."
## [1] "BKR.Open has zero or negative values. Log series will be zero."
## [1] "BKR.Volume has zero or negative values. Log series will be zero."
## [1] "HAL.Open has zero or negative values. Log series will be zero."
## [1] "HAL.Volume has zero or negative values. Log series will be zero."
## [1] "IP.Open has zero or negative values. Log series will be zero."
## [1] "T.Open has zero or negative values. Log series will be zero."
## [1] "OPEARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "AREARNINGSPERSHARE has zero or negative values. Log series will be zero."
## [1] "OCCEquityVolume has zero or negative values. Log series will be zero."
## [1] "OCCNonEquityVolume has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA has zero or negative values. Log series will be zero."
## [1] "BUSLOANS.minus.BUSLOANSNSA.by.GDP has zero or negative values. Log series will be zero."
## [1] "EXPCH.minus.IMPCH has zero or negative values. Log series will be zero."
## [1] "EXPMX.minus.IMPMX has zero or negative values. Log series will be zero."
## [1] "SRPSABSNNCB.by.GDP has zero or negative values. Log series will be zero."
## [1] "DGS30TO10 has zero or negative values. Log series will be zero."
## [1] "DGS10TO1 has zero or negative values. Log series will be zero."
## [1] "DGS10TO2 has zero or negative values. Log series will be zero."
## [1] "DGS10TOTB3MS has zero or negative values. Log series will be zero."
## [1] "DGS10TODTB3 has zero or negative values. Log series will be zero."
## [1] "DCOILWTICO.by.PPIACO has zero or negative values. Log series will be zero."
## [1] "GSPC.DailySwing has zero or negative values. Log series will be zero."
df.data <- lst.df[[1]]
df.symbols <- lst.df[[2]]

Recession calculations

Summary calculations

These values are used below

Conclusion

In this worksheet a model predicting the onset of recession was built. From the model a trading rule was derived to allow backtesting. The model performed well and the trading rule backtesting showed that applying this in the post-WWII period would have resulted in an increase in returns. That is not too bad, but there are a few changes that would likely improve the model:

Market Conditions

#The model is predicting a `r paste(sprintf("%3.0f", tail(df.data$recession.initiation.smooth.avg,1)[[1]]*100), "%", sep="")` chance of recession in the next 12 months. :

#- P/E ratio of `r sprintf("%3.2f", tail(df.data$MULTPLSP500PERATIOMONTH,1))` compares to a historical mean value over the last decade of `r sprintf("%3.2f", df.data$MULTPLSP500PERATIOMONTH_Mean[1])`. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

As of Feb 2020 we have entered a recession as defined by the NBER yet the market continues to rise.

P/E ratio of 30.02 compares to a historical mean value over the last decade of 18.61. Since 2008 recession P/E has only fallen below historical norm a few times. The current value is high, but well off the peaks. If earnings are +2-4% year-over-year then it is not unrealistic.

  • S&P 500 Volume, last updated on 2021-12-31, is negative over the last year and negative over the last month.

Unemployment

  • Headline unemployment (U-3) stands at 4.20% (last updated on 2021-11-01) which is near the 1-year average of 5.30% and rising with respect to the low in the last twelve months of 4.20%. Unlikely the rate will drop again.

  • Payrolls (BLS data, NSA) year-over-year stands at 6.34% which is above the 1-year average of 3.48% and falling with respect to the peak, in the last twelve months, of 10.86%.

  • Jobless claims (ICSA data) year-over-year stands at -74.48% (last updated on 2021-12-25) which is in-line with the 1-year average of -0.03% and below the peak, in the last twelve months, of 337.62%.
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_hline).
## Warning: Removed 1 rows containing missing values (geom_text).
## Warning: Removed 1 rows containing missing values (geom_hline).

Personal Income

  • Real personal income year over year growth stands at 1.94% (last updated on 2021-11-01). This is below the recent peak of 8.49%.

Yield Curve and Bond Market

  • The 10-year to 3-month yield stands at 1.50% (last updated on 2021-12-29). This is above the recent low of 0.84%. The trend is flat over the last year and positive over the last month.

  • Auto sales flat?

Auxillary Series

I explored additional data series. The sections below have those data series along with comments.

Recent Highs

Print out the new 180 day high values

df.symbolsTrue <-
  df.symbols[df.symbols$'Max180' == TRUE, c("string.symbol", "string.description")]
df.symbolsTrue <-
  df.symbolsTrue[!(is.na(df.symbolsTrue$string.symbol)), ]
df.symbolsTrue <-
  df.symbolsTrue[!(df.symbolsTrue$string.symbol == 'USREC'), ]
#print(head(df.symbolsTrue,20))

kable(df.symbolsTrue, caption = "6-Month High") %>%
  kable_styling(bootstrap_options = c("striped", "hover"))  
6-Month High
string.symbol string.description
1 CPIAUCSL Consumer Price Index for All Urban Consumers: All Items
4 PCEPI Personal Consumption Expenditures: Chain-type Price Index
7 NPPTTL Total Nonfarm Private Payroll Employment (ADP)
9 PAYNSA All Employees: Total Nonfarm Payrolls (NSA)
10 TABSHNO Households and nonprofit organizations; total assets, Level
11 HNONWPDPI Household Net Worth, percent Dispsable Income
12 INDPRO Industrial Production Index
14 RSALES Real Retail Sales (DISCONTINUED)
49 IMPCH U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
50 EXPCH U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA)
51 IMPMX U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA)
52 EXPMX U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA)
54 HNFSUSNSA New One Family Houses for Sale in the United States (Monthly, NSA)
56 TOTCI Commercial and Industrial Loans, All Commercial Banks (Weekly, SA)
58 REALLNNSA Real Estate Loans, All Commercial Banks (Monthly, NSA)
59 REALLN Real Estate Loans, All Commercial Banks (Monthly, SA)
60 RELACBW027NBOG Real Estate Loans, All Commercial Banks (Weekly, NSA)
61 RELACBW027SBOG Real Estate Loans, All Commercial Banks (Weekly, SA)
62 RREACBM027NBOG Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
63 RREACBM027SBOG Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
64 RREACBW027SBOG Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
67 CONSUMERNSA Consumer Loans, All Commercial Banks
68 TOTLLNSA Loans and Leases in Bank Credit, All Commercial Banks
69 DPSACBW027SBOG Deposits, All Commercial Banks
70 DRCLACBS Delinquency Rate on Consumer Loans, All Commercial Banks, SA
71 TOTCINSA Commercial and Industrial Loans, All Commercial Banks (Weekly, NSA)
72 SRPSABSNNCB Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
73 ASTLL All sectors; total loans; liability, Level (NSA)
74 FBDILNECA Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
75 ASOLAL All sectors; other loans and advances; liability, Level (NSA)
76 ASTMA All sectors; total mortgages; asset, Level (NSA)
77 ASHMA All sectors; home mortgages; asset, Level (NSA)
78 ASMRMA All sectors; multifamily residential mortgages; asset, Level (NSA)
79 ASCMA All sectors; commercial mortgages; asset, Level (NSA)
80 ASFMA All sectors; farm mortgages; asset, Level (NSA)
81 CCLBSHNO Households and nonprofit organizations; consumer credit; liability, Level (NSA)
82 FBDSILQ027S Domestic financial sectors debt securities; liability, Level (NSA)
83 FBLL Domestic financial sectors loans; liability, Level (NSA)
84 NCBDBIQ027S Nonfinancial corporate business; debt securities; liability, Level
91 TB3MS 3-Month Treasury Bill: Secondary Market Rate (Monthly)
104 GDP Gross Domestic Product
105 FNDEFX Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate)
106 FDEFX Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
107 GDPNOW Fed Atlanta GDPNow
108 GDPC1 Real Gross Domestic Product
109 GDPDEF Gross Domestic Product: Implicit Price Deflator
113 GPDI Gross Private Domestic Investment
114 W790RC1Q027SBEA Net domestic investment: Private: Domestic busines
115 MZMV Velocity of MZM Money Stock
116 M1 M1 Money Stock
117 M2 M2 Money Stock
118 OPHNFB Nonfarm Business Sector: Real Output Per Hour of All Persons
119 IPMAN Industrial Production: Manufacturing (NAICS)
121 GS5 5-Year Treasury Constant Maturity Rate
125 HOUST1F Privately Owned Housing Starts: 1-Unit Structures
126 GFDEBTN Federal Debt: Total Public Debt
127 HOUST Housing Starts: Total: New Privately Owned Housing Units Started
128 MSPUS Median Sales Price of Houses Sold for the United States
130 DGORDER Manufacturers’ New Orders: Durable Goods (SA)
131 CSUSHPINSA S&P/Case-Shiller U.S. National Home Price Index (NSA)
132 GFDEGDQ188S Federal Debt: Total Public Debt as Percent of Gross Domestic Product
133 FYFSD Federal Surplus or Deficit
134 FYFSGDA188S Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
140 OUTMS Manufacturing Sector: Real Output
141 MANEMP All Employees: Manufacturing
142 PRS30006163 Manufacturing Sector: Real Output Per Person
145 SOFR Secured Overnight Financing Rate
147 SOFR99 Secured Overnight Financing Rate: 99th Percentile
155 OBFR1 Overnight Bank Funding Rate: 1st Percentile
157 IOER Interest Rate on Excess Reserves
159 EXCSRESNW Excess Reserves of Depository Institutions
160 ECBASSETS Central Bank Assets for Euro Area (11-19 Countries)
161 EUNNGDP Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
164 CURRENCY Currency Component of M1 (Seasonally Adjusted)
165 WCURRNS Currency Component of M1
166 BOGMBASE Monetary Base; Total
167 PRS88003193 Nonfinancial Corporations Sector: Unit Profits
168 PPIACO Producer Price Index for All Commodities
169 PCUOMFGOMFG Producer Price Index by Industry: Total Manufacturing Industries
170 POPTHM Population (U.S.)
171 POPTHM Population (U.S.)
172 CLF16OV Civilian Labor Force Level, SA
176 RSAFS Advance Retail Sales: Retail and Food Services
178 BOPGTB Trade Balance: Goods, Balance of Payments Basis (SA)
180 A065RC1A027NBEA Personal income (NSA)
182 PCE Personal Consumption Expenditures (SA)
183 A053RC1Q027SBEA National income: Corporate profits before tax (without IVA and CCAdj)
184 CPROFIT Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj)
217 ISMMANPMI Institute of Supply Managment PMI Composite Index
219 MULTPLSP500SALESQUARTER S&P 500 TTM Sales (Not Inflation Adjusted)
221 MULTPLSP500DIVMONTH S&P 500 Dividend by Month (Inflation Adjusted)
222 CHRISCMEHG1 Copper Futures, Continuous Contract #1 (HG1) (Front Month)
223 WWDIWLDISAIRGOODMTK1 Air transport, freight
224 BKRTotal Total Rig Count
225 BKRGas Gas Rig Count
226 BKROil Oil Rig Count
227 FARMINCOME Net Farm Income
228 OPEARNINGSPERSHARE Operating Earnings per Share
229 AREARNINGSPERSHARE As-Reported Earnings per Share
230 CASHDIVIDENDSPERSHR Cash Dividends per Share
231 SALESPERSHR Sales per Share
232 BOOKVALPERSHR Book value per Share
233 CAPEXPERSHR Cap ex per Share
234 PRICE Price
235 OPEARNINGSTTM TTM Operating Earnings
236 AREARNINGSTTM TTM Reported Earnings
239 OCCEquityVolume Equity Options Volume
240 OCCNonEquityVolume Non-Equity Options Volume
248 TOTCI.by.GDP Business Loans (Weekly, SA) Normalized by GDP
249 TOTCINSA.by.GDP Business Loans (Weekly, NSA) Normalized by GDP
253 A065RC1A027NBEA.by.GDP Personal Income (NSA) Normalized by GDP
255 A053RC1Q027SBEA.by.GDP National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
256 CPROFIT.by.GDP National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
257 CONSUMERNSA.by.GDP Consumer Loans Not Seasonally Adjusted divided by GDP
258 RREACBM027NBOG.by.GDP Residental Real Estate Loans (Monthly, NSA) divided by GDP
259 RREACBM027SBOG.by.GDP Residental Real Estate Loans (Monthly, SA) divided by GDP
260 RREACBW027SBOG.by.GDP Residental Real Estate Loans (Weekly, SA) divided by GDP
263 DGORDER.by.GDP Durable Goods (Monthly, NSA) divided by GDP
264 ASHMA.by.GDP Home Mortgages (Quarterly, NSA) divided by GDP
267 CONSUMERNSA.INTEREST Consumer Loans (Not Seasonally Adjusted) Interest Burdens
268 CONSUMERNSA.INTEREST.by.GDP Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
269 TOTLNNSA Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
270 TOTLNNSA.by.GDP Total Loans Not Seasonally Adjusted divided by GDP
274 EXCSRESNW.by.GDP Excess Reserves of Depository Institutions Divided by GDP
279 SRPSABSNNCB.by.GDP Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
280 ASTLL.by.GDP All sectors; total loans; liability, Level (NSA) Divided by GDP
281 ASFMA.by.GDP All sectors; farm mortgages; asset, Level (NSA) Divided by GDP
282 ASFMA.by.ASTLL All sectors; total loans Divided by farm mortgages
285 FARMINCOME.by.GDP Farm Income (Annual, NSA) Divided by GDP
286 BOGMBASE.by.GDP BOGMBASE Divided by GDP
288 ECBASSETS.by.EUNNGDP Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP
297 NPPTTLBYPOPTHM ADP Private Employment / Population
316 HNFSUSNSA.minus.HSN1FNSA Houses for sale - houses sold
317 MSPUS.times.HOUST New privately owned units start times median price
318 MSPUS.times.HNFSUSNSA New privately owned 1-family units for sale times median price
322 CPIAUCSL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Consumer Price Index for All Urban Consumers: All Items
325 CPIAUCSL_Log Log of Consumer Price Index for All Urban Consumers: All Items
326 CPIAUCSL_mva200 Consumer Price Index for All Urban Consumers: All Items 200 Day MA
327 CPIAUCSL_mva050 Consumer Price Index for All Urban Consumers: All Items 50 Day MA
328 USREC_YoY NBER based Recession Indicators Year over Year
329 USREC_YoY4 NBER based Recession Indicators 4 Year over 4 Year
330 USREC_YoY5 NBER based Recession Indicators 5 Year over 5 Year
331 USREC_Smooth Savitsky-Golay Smoothed (p=3, n=365) NBER based Recession Indicators
332 USREC_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) NBER based Recession Indicators
333 USREC_SmoothDer Derivative of Smoothed NBER based Recession Indicators
334 USREC_Log Log of NBER based Recession Indicators
335 USREC_mva200 NBER based Recession Indicators 200 Day MA
336 USREC_mva050 NBER based Recession Indicators 50 Day MA
349 PCEPI_Smooth Savitsky-Golay Smoothed (p=3, n=365) Personal Consumption Expenditures: Chain-type Price Index
352 PCEPI_Log Log of Personal Consumption Expenditures: Chain-type Price Index
353 PCEPI_mva200 Personal Consumption Expenditures: Chain-type Price Index 200 Day MA
354 PCEPI_mva050 Personal Consumption Expenditures: Chain-type Price Index 50 Day MA
376 NPPTTL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Total Nonfarm Private Payroll Employment (ADP)
379 NPPTTL_Log Log of Total Nonfarm Private Payroll Employment (ADP)
380 NPPTTL_mva200 Total Nonfarm Private Payroll Employment (ADP) 200 Day MA
381 NPPTTL_mva050 Total Nonfarm Private Payroll Employment (ADP) 50 Day MA
382 U6RATE_YoY Total unemployed + margin + part-time U-6 Year over Year
387 U6RATE_SmoothDer Derivative of Smoothed Total unemployed + margin + part-time U-6
391 PAYNSA_YoY All Employees: Total Nonfarm Payrolls (NSA) Year over Year
392 PAYNSA_YoY4 All Employees: Total Nonfarm Payrolls (NSA) 4 Year over 4 Year
393 PAYNSA_YoY5 All Employees: Total Nonfarm Payrolls (NSA) 5 Year over 5 Year
394 PAYNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) All Employees: Total Nonfarm Payrolls (NSA)
397 PAYNSA_Log Log of All Employees: Total Nonfarm Payrolls (NSA)
398 PAYNSA_mva200 All Employees: Total Nonfarm Payrolls (NSA) 200 Day MA
399 PAYNSA_mva050 All Employees: Total Nonfarm Payrolls (NSA) 50 Day MA
406 TABSHNO_Log Log of Households and nonprofit organizations; total assets, Level
407 TABSHNO_mva200 Households and nonprofit organizations; total assets, Level 200 Day MA
408 TABSHNO_mva050 Households and nonprofit organizations; total assets, Level 50 Day MA
409 HNONWPDPI_YoY Household Net Worth, percent Dispsable Income Year over Year
412 HNONWPDPI_Smooth Savitsky-Golay Smoothed (p=3, n=365) Household Net Worth, percent Dispsable Income
415 HNONWPDPI_Log Log of Household Net Worth, percent Dispsable Income
416 HNONWPDPI_mva200 Household Net Worth, percent Dispsable Income 200 Day MA
417 HNONWPDPI_mva050 Household Net Worth, percent Dispsable Income 50 Day MA
421 INDPRO_Smooth Savitsky-Golay Smoothed (p=3, n=365) Industrial Production Index
424 INDPRO_Log Log of Industrial Production Index
425 INDPRO_mva200 Industrial Production Index 200 Day MA
426 INDPRO_mva050 Industrial Production Index 50 Day MA
430 RRSFS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Retail and Food Services Sales
436 RSALES_YoY Real Retail Sales (DISCONTINUED) Year over Year
437 RSALES_YoY4 Real Retail Sales (DISCONTINUED) 4 Year over 4 Year
438 RSALES_YoY5 Real Retail Sales (DISCONTINUED) 5 Year over 5 Year
442 RSALES_Log Log of Real Retail Sales (DISCONTINUED)
443 RSALES_mva200 Real Retail Sales (DISCONTINUED) 200 Day MA
444 RSALES_mva050 Real Retail Sales (DISCONTINUED) 50 Day MA
452 W875RX1_mva200 Real personal income excluding current transfer receipts 200 Day MA
459 RPI_SmoothDer Derivative of Smoothed Real personal income
466 PCOPPUSDM_Smooth Savitsky-Golay Smoothed (p=3, n=365) Global price of Copper
475 NOBL.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
476 NOBL.Open_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
479 NOBL.Open_mva200 200 Day MA
480 NOBL.Open_mva050 50 Day MA
484 NOBL.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
485 NOBL.High_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
487 NOBL.High_Log Log of
488 NOBL.High_mva200 200 Day MA
489 NOBL.High_mva050 50 Day MA
493 NOBL.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
494 NOBL.Low_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
497 NOBL.Low_mva200 200 Day MA
498 NOBL.Low_mva050 50 Day MA
502 NOBL.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
505 NOBL.Close_Log Log of
506 NOBL.Close_mva200 200 Day MA
507 NOBL.Close_mva050 50 Day MA
511 NOBL.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
513 NOBL.Volume_SmoothDer Derivative of Smoothed
516 NOBL.Volume_mva050 50 Day MA
520 NOBL.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
523 NOBL.Adjusted_Log Log of
524 NOBL.Adjusted_mva200 200 Day MA
525 NOBL.Adjusted_mva050 50 Day MA
529 SCHD.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
533 SCHD.Open_mva200 200 Day MA
534 SCHD.Open_mva050 50 Day MA
538 SCHD.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
542 SCHD.High_mva200 200 Day MA
543 SCHD.High_mva050 50 Day MA
547 SCHD.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
550 SCHD.Low_Log Log of
551 SCHD.Low_mva200 200 Day MA
552 SCHD.Low_mva050 50 Day MA
556 SCHD.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
560 SCHD.Close_mva200 200 Day MA
561 SCHD.Close_mva050 50 Day MA
565 SCHD.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
567 SCHD.Volume_SmoothDer Derivative of Smoothed
570 SCHD.Volume_mva050 50 Day MA
574 SCHD.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
578 SCHD.Adjusted_mva200 200 Day MA
579 SCHD.Adjusted_mva050 50 Day MA
581 PFF.Open_YoY4 4 Year over 4 Year
582 PFF.Open_YoY5 5 Year over 5 Year
587 PFF.Open_mva200 200 Day MA
590 PFF.High_YoY4 4 Year over 4 Year
596 PFF.High_mva200 200 Day MA
599 PFF.Low_YoY4 4 Year over 4 Year
605 PFF.Low_mva200 200 Day MA
608 PFF.Close_YoY4 4 Year over 4 Year
614 PFF.Close_mva200 200 Day MA
621 PFF.Volume_SmoothDer Derivative of Smoothed
626 PFF.Adjusted_YoY4 4 Year over 4 Year
629 PFF.Adjusted_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
631 PFF.Adjusted_Log Log of
632 PFF.Adjusted_mva200 200 Day MA
675 HPI.Volume_SmoothDer Derivative of Smoothed
691 GSFTX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
695 GSFTX.Open_mva200 200 Day MA
700 GSFTX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
704 GSFTX.High_mva200 200 Day MA
709 GSFTX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
713 GSFTX.Low_mva200 200 Day MA
718 GSFTX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
722 GSFTX.Close_mva200 200 Day MA
724 GSFTX.Volume_YoY Year over Year
725 GSFTX.Volume_YoY4 4 Year over 4 Year
726 GSFTX.Volume_YoY5 5 Year over 5 Year
727 GSFTX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
728 GSFTX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
729 GSFTX.Volume_SmoothDer Derivative of Smoothed
730 GSFTX.Volume_Log Log of
731 GSFTX.Volume_mva200 200 Day MA
732 GSFTX.Volume_mva050 50 Day MA
736 GSFTX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
740 GSFTX.Adjusted_mva200 200 Day MA
741 GSFTX.Adjusted_mva050 50 Day MA
778 LFMIX.Volume_YoY Year over Year
779 LFMIX.Volume_YoY4 4 Year over 4 Year
780 LFMIX.Volume_YoY5 5 Year over 5 Year
781 LFMIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
782 LFMIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
783 LFMIX.Volume_SmoothDer Derivative of Smoothed
784 LFMIX.Volume_Log Log of
785 LFMIX.Volume_mva200 200 Day MA
786 LFMIX.Volume_mva050 50 Day MA
832 LFMCX.Volume_YoY Year over Year
833 LFMCX.Volume_YoY4 4 Year over 4 Year
834 LFMCX.Volume_YoY5 5 Year over 5 Year
835 LFMCX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
836 LFMCX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
837 LFMCX.Volume_SmoothDer Derivative of Smoothed
838 LFMCX.Volume_Log Log of
839 LFMCX.Volume_mva200 200 Day MA
840 LFMCX.Volume_mva050 50 Day MA
886 LFMAX.Volume_YoY Year over Year
887 LFMAX.Volume_YoY4 4 Year over 4 Year
888 LFMAX.Volume_YoY5 5 Year over 5 Year
889 LFMAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
890 LFMAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
891 LFMAX.Volume_SmoothDer Derivative of Smoothed
892 LFMAX.Volume_Log Log of
893 LFMAX.Volume_mva200 200 Day MA
894 LFMAX.Volume_mva050 50 Day MA
940 LCSIX.Volume_YoY Year over Year
941 LCSIX.Volume_YoY4 4 Year over 4 Year
942 LCSIX.Volume_YoY5 5 Year over 5 Year
943 LCSIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
944 LCSIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
945 LCSIX.Volume_SmoothDer Derivative of Smoothed
946 LCSIX.Volume_Log Log of
947 LCSIX.Volume_mva200 200 Day MA
948 LCSIX.Volume_mva050 50 Day MA
1001 BSV.Volume_mva200 200 Day MA
1048 VBIRX.Volume_YoY Year over Year
1049 VBIRX.Volume_YoY4 4 Year over 4 Year
1050 VBIRX.Volume_YoY5 5 Year over 5 Year
1051 VBIRX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1052 VBIRX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1053 VBIRX.Volume_SmoothDer Derivative of Smoothed
1054 VBIRX.Volume_Log Log of
1055 VBIRX.Volume_mva200 200 Day MA
1056 VBIRX.Volume_mva050 50 Day MA
1105 BIV.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1107 BIV.Volume_SmoothDer Derivative of Smoothed
1156 VFSUX.Volume_YoY Year over Year
1157 VFSUX.Volume_YoY4 4 Year over 4 Year
1158 VFSUX.Volume_YoY5 5 Year over 5 Year
1159 VFSUX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1160 VFSUX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1161 VFSUX.Volume_SmoothDer Derivative of Smoothed
1162 VFSUX.Volume_Log Log of
1163 VFSUX.Volume_mva200 200 Day MA
1164 VFSUX.Volume_mva050 50 Day MA
1210 LTUIX.Volume_YoY Year over Year
1211 LTUIX.Volume_YoY4 4 Year over 4 Year
1212 LTUIX.Volume_YoY5 5 Year over 5 Year
1213 LTUIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1214 LTUIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1215 LTUIX.Volume_SmoothDer Derivative of Smoothed
1216 LTUIX.Volume_Log Log of
1217 LTUIX.Volume_mva200 200 Day MA
1218 LTUIX.Volume_mva050 50 Day MA
1264 PTTPX.Volume_YoY Year over Year
1265 PTTPX.Volume_YoY4 4 Year over 4 Year
1266 PTTPX.Volume_YoY5 5 Year over 5 Year
1267 PTTPX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1268 PTTPX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1269 PTTPX.Volume_SmoothDer Derivative of Smoothed
1270 PTTPX.Volume_Log Log of
1271 PTTPX.Volume_mva200 200 Day MA
1272 PTTPX.Volume_mva050 50 Day MA
1318 NERYX.Volume_YoY Year over Year
1319 NERYX.Volume_YoY4 4 Year over 4 Year
1320 NERYX.Volume_YoY5 5 Year over 5 Year
1321 NERYX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1322 NERYX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1323 NERYX.Volume_SmoothDer Derivative of Smoothed
1324 NERYX.Volume_Log Log of
1325 NERYX.Volume_mva200 200 Day MA
1326 NERYX.Volume_mva050 50 Day MA
1341 STIGX.Open_SmoothDer Derivative of Smoothed
1350 STIGX.High_SmoothDer Derivative of Smoothed
1359 STIGX.Low_SmoothDer Derivative of Smoothed
1368 STIGX.Close_SmoothDer Derivative of Smoothed
1372 STIGX.Volume_YoY Year over Year
1373 STIGX.Volume_YoY4 4 Year over 4 Year
1374 STIGX.Volume_YoY5 5 Year over 5 Year
1375 STIGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1376 STIGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1377 STIGX.Volume_SmoothDer Derivative of Smoothed
1378 STIGX.Volume_Log Log of
1379 STIGX.Volume_mva200 200 Day MA
1380 STIGX.Volume_mva050 50 Day MA
1388 STIGX.Adjusted_mva200 200 Day MA
1426 HLGAX.Volume_YoY Year over Year
1427 HLGAX.Volume_YoY4 4 Year over 4 Year
1428 HLGAX.Volume_YoY5 5 Year over 5 Year
1429 HLGAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1430 HLGAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1431 HLGAX.Volume_SmoothDer Derivative of Smoothed
1432 HLGAX.Volume_Log Log of
1433 HLGAX.Volume_mva200 200 Day MA
1434 HLGAX.Volume_mva050 50 Day MA
1480 FTRGX.Volume_YoY Year over Year
1481 FTRGX.Volume_YoY4 4 Year over 4 Year
1482 FTRGX.Volume_YoY5 5 Year over 5 Year
1483 FTRGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1484 FTRGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1485 FTRGX.Volume_SmoothDer Derivative of Smoothed
1486 FTRGX.Volume_Log Log of
1487 FTRGX.Volume_mva200 200 Day MA
1488 FTRGX.Volume_mva050 50 Day MA
1534 THIIX.Volume_YoY Year over Year
1535 THIIX.Volume_YoY4 4 Year over 4 Year
1536 THIIX.Volume_YoY5 5 Year over 5 Year
1537 THIIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1538 THIIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1539 THIIX.Volume_SmoothDer Derivative of Smoothed
1540 THIIX.Volume_Log Log of
1541 THIIX.Volume_mva200 200 Day MA
1542 THIIX.Volume_mva050 50 Day MA
1588 PTTRX.Volume_YoY Year over Year
1589 PTTRX.Volume_YoY4 4 Year over 4 Year
1590 PTTRX.Volume_YoY5 5 Year over 5 Year
1591 PTTRX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1592 PTTRX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1593 PTTRX.Volume_SmoothDer Derivative of Smoothed
1594 PTTRX.Volume_Log Log of
1595 PTTRX.Volume_mva200 200 Day MA
1596 PTTRX.Volume_mva050 50 Day MA
1642 BFIGX.Volume_YoY Year over Year
1643 BFIGX.Volume_YoY4 4 Year over 4 Year
1644 BFIGX.Volume_YoY5 5 Year over 5 Year
1645 BFIGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1646 BFIGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1647 BFIGX.Volume_SmoothDer Derivative of Smoothed
1648 BFIGX.Volume_Log Log of
1649 BFIGX.Volume_mva200 200 Day MA
1650 BFIGX.Volume_mva050 50 Day MA
1658 BFIGX.Adjusted_mva200 200 Day MA
1663 VTWO.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1672 VTWO.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1681 VTWO.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1690 VTWO.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1699 VTWO.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1701 VTWO.Volume_SmoothDer Derivative of Smoothed
1703 VTWO.Volume_mva200 200 Day MA
1708 VTWO.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1719 EIFAX.Open_SmoothDer Derivative of Smoothed
1728 EIFAX.High_SmoothDer Derivative of Smoothed
1737 EIFAX.Low_SmoothDer Derivative of Smoothed
1746 EIFAX.Close_SmoothDer Derivative of Smoothed
1750 EIFAX.Volume_YoY Year over Year
1751 EIFAX.Volume_YoY4 4 Year over 4 Year
1752 EIFAX.Volume_YoY5 5 Year over 5 Year
1753 EIFAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1754 EIFAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1755 EIFAX.Volume_SmoothDer Derivative of Smoothed
1756 EIFAX.Volume_Log Log of
1757 EIFAX.Volume_mva200 200 Day MA
1758 EIFAX.Volume_mva050 50 Day MA
1766 EIFAX.Adjusted_mva200 200 Day MA
1804 ASDAX.Volume_YoY Year over Year
1805 ASDAX.Volume_YoY4 4 Year over 4 Year
1806 ASDAX.Volume_YoY5 5 Year over 5 Year
1807 ASDAX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1808 ASDAX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1809 ASDAX.Volume_SmoothDer Derivative of Smoothed
1810 ASDAX.Volume_Log Log of
1811 ASDAX.Volume_mva200 200 Day MA
1812 ASDAX.Volume_mva050 50 Day MA
1858 TRBUX.Volume_YoY Year over Year
1859 TRBUX.Volume_YoY4 4 Year over 4 Year
1860 TRBUX.Volume_YoY5 5 Year over 5 Year
1861 TRBUX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1862 TRBUX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1863 TRBUX.Volume_SmoothDer Derivative of Smoothed
1864 TRBUX.Volume_Log Log of
1865 TRBUX.Volume_mva200 200 Day MA
1866 TRBUX.Volume_mva050 50 Day MA
1912 PRWCX.Volume_YoY Year over Year
1913 PRWCX.Volume_YoY4 4 Year over 4 Year
1914 PRWCX.Volume_YoY5 5 Year over 5 Year
1915 PRWCX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1916 PRWCX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1917 PRWCX.Volume_SmoothDer Derivative of Smoothed
1918 PRWCX.Volume_Log Log of
1919 PRWCX.Volume_mva200 200 Day MA
1920 PRWCX.Volume_mva050 50 Day MA
1924 PRWCX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1928 PRWCX.Adjusted_mva200 200 Day MA
1929 PRWCX.Adjusted_mva050 50 Day MA
1966 ADOZX.Volume_YoY Year over Year
1967 ADOZX.Volume_YoY4 4 Year over 4 Year
1968 ADOZX.Volume_YoY5 5 Year over 5 Year
1969 ADOZX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
1970 ADOZX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
1971 ADOZX.Volume_SmoothDer Derivative of Smoothed
1972 ADOZX.Volume_Log Log of
1973 ADOZX.Volume_mva200 200 Day MA
1974 ADOZX.Volume_mva050 50 Day MA
1989 MERFX.Open_SmoothDer Derivative of Smoothed
1998 MERFX.High_SmoothDer Derivative of Smoothed
2007 MERFX.Low_SmoothDer Derivative of Smoothed
2016 MERFX.Close_SmoothDer Derivative of Smoothed
2020 MERFX.Volume_YoY Year over Year
2021 MERFX.Volume_YoY4 4 Year over 4 Year
2022 MERFX.Volume_YoY5 5 Year over 5 Year
2023 MERFX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2024 MERFX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2025 MERFX.Volume_SmoothDer Derivative of Smoothed
2026 MERFX.Volume_Log Log of
2027 MERFX.Volume_mva200 200 Day MA
2028 MERFX.Volume_mva050 50 Day MA
2041 CMNIX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2044 CMNIX.Open_Log Log of
2045 CMNIX.Open_mva200 200 Day MA
2046 CMNIX.Open_mva050 50 Day MA
2050 CMNIX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2053 CMNIX.High_Log Log of
2054 CMNIX.High_mva200 200 Day MA
2055 CMNIX.High_mva050 50 Day MA
2059 CMNIX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2062 CMNIX.Low_Log Log of
2063 CMNIX.Low_mva200 200 Day MA
2064 CMNIX.Low_mva050 50 Day MA
2068 CMNIX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2071 CMNIX.Close_Log Log of
2072 CMNIX.Close_mva200 200 Day MA
2073 CMNIX.Close_mva050 50 Day MA
2074 CMNIX.Volume_YoY Year over Year
2075 CMNIX.Volume_YoY4 4 Year over 4 Year
2076 CMNIX.Volume_YoY5 5 Year over 5 Year
2077 CMNIX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2078 CMNIX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2079 CMNIX.Volume_SmoothDer Derivative of Smoothed
2080 CMNIX.Volume_Log Log of
2081 CMNIX.Volume_mva200 200 Day MA
2082 CMNIX.Volume_mva050 50 Day MA
2086 CMNIX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2089 CMNIX.Adjusted_Log Log of
2090 CMNIX.Adjusted_mva200 200 Day MA
2091 CMNIX.Adjusted_mva050 50 Day MA
2095 CIHEX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2099 CIHEX.Open_mva200 200 Day MA
2100 CIHEX.Open_mva050 50 Day MA
2104 CIHEX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2108 CIHEX.High_mva200 200 Day MA
2109 CIHEX.High_mva050 50 Day MA
2113 CIHEX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2117 CIHEX.Low_mva200 200 Day MA
2118 CIHEX.Low_mva050 50 Day MA
2122 CIHEX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2126 CIHEX.Close_mva200 200 Day MA
2127 CIHEX.Close_mva050 50 Day MA
2128 CIHEX.Volume_YoY Year over Year
2129 CIHEX.Volume_YoY4 4 Year over 4 Year
2130 CIHEX.Volume_YoY5 5 Year over 5 Year
2131 CIHEX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2132 CIHEX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2133 CIHEX.Volume_SmoothDer Derivative of Smoothed
2134 CIHEX.Volume_Log Log of
2135 CIHEX.Volume_mva200 200 Day MA
2136 CIHEX.Volume_mva050 50 Day MA
2140 CIHEX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2144 CIHEX.Adjusted_mva200 200 Day MA
2145 CIHEX.Adjusted_mva050 50 Day MA
2146 IMPCH_YoY U.S. Imports of Goods by Customs Basis from China (Monthly, NSA) Year over Year
2152 IMPCH_Log Log of U.S. Imports of Goods by Customs Basis from China (Monthly, NSA)
2153 IMPCH_mva200 U.S. Imports of Goods by Customs Basis from China (Monthly, NSA) 200 Day MA
2154 IMPCH_mva050 U.S. Imports of Goods by Customs Basis from China (Monthly, NSA) 50 Day MA
2155 EXPCH_YoY U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA) Year over Year
2156 EXPCH_YoY4 U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA) 4 Year over 4 Year
2157 EXPCH_YoY5 U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA) 5 Year over 5 Year
2158 EXPCH_Smooth Savitsky-Golay Smoothed (p=3, n=365) U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA)
2160 EXPCH_SmoothDer Derivative of Smoothed U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA)
2161 EXPCH_Log Log of U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA)
2162 EXPCH_mva200 U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA) 200 Day MA
2163 EXPCH_mva050 U.S. Exports of Goods by F.A.S. Basis to China, Mainland (Monthly, NSA) 50 Day MA
2164 IMPMX_YoY U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA) Year over Year
2166 IMPMX_YoY5 U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA) 5 Year over 5 Year
2167 IMPMX_Smooth Savitsky-Golay Smoothed (p=3, n=365) U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA)
2170 IMPMX_Log Log of U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA)
2171 IMPMX_mva200 U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA) 200 Day MA
2172 IMPMX_mva050 U.S. Imports of Goods by Customs Basis from Mexico (Monthly, NSA) 50 Day MA
2176 EXPMX_Smooth Savitsky-Golay Smoothed (p=3, n=365) U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA)
2179 EXPMX_Log Log of U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA)
2180 EXPMX_mva200 U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA) 200 Day MA
2181 EXPMX_mva050 U.S. Exports of Goods by F.A.S. Basis to Mexico (Monthly, NSA) 50 Day MA
2187 HSN1FNSA_SmoothDer Derivative of Smoothed New One Family Houses Sold: United States (Monthly, NSA)
2197 HNFSUSNSA_Log Log of New One Family Houses for Sale in the United States (Monthly, NSA)
2198 HNFSUSNSA_mva200 New One Family Houses for Sale in the United States (Monthly, NSA) 200 Day MA
2199 HNFSUSNSA_mva050 New One Family Houses for Sale in the United States (Monthly, NSA) 50 Day MA
2200 BUSLOANS_YoY Commercial and Industrial Loans, All Commercial Banks (Monthly, SA) Year over Year
2203 BUSLOANS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Commercial and Industrial Loans, All Commercial Banks (Monthly, SA)
2205 BUSLOANS_SmoothDer Derivative of Smoothed Commercial and Industrial Loans, All Commercial Banks (Monthly, SA)
2209 TOTCI_YoY Commercial and Industrial Loans, All Commercial Banks (Weekly, SA) Year over Year
2211 TOTCI_YoY5 Commercial and Industrial Loans, All Commercial Banks (Weekly, SA) 5 Year over 5 Year
2212 TOTCI_Smooth Savitsky-Golay Smoothed (p=3, n=365) Commercial and Industrial Loans, All Commercial Banks (Weekly, SA)
2214 TOTCI_SmoothDer Derivative of Smoothed Commercial and Industrial Loans, All Commercial Banks (Weekly, SA)
2215 TOTCI_Log Log of Commercial and Industrial Loans, All Commercial Banks (Weekly, SA)
2218 BUSLOANSNSA_YoY Commercial and Industrial Loans, All Commercial Banks (Monthly, NSA) Year over Year
2221 BUSLOANSNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Commercial and Industrial Loans, All Commercial Banks (Monthly, NSA)
2223 BUSLOANSNSA_SmoothDer Derivative of Smoothed Commercial and Industrial Loans, All Commercial Banks (Monthly, NSA)
2227 REALLNNSA_YoY Real Estate Loans, All Commercial Banks (Monthly, NSA) Year over Year
2232 REALLNNSA_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Monthly, NSA)
2233 REALLNNSA_Log Log of Real Estate Loans, All Commercial Banks (Monthly, NSA)
2234 REALLNNSA_mva200 Real Estate Loans, All Commercial Banks (Monthly, NSA) 200 Day MA
2235 REALLNNSA_mva050 Real Estate Loans, All Commercial Banks (Monthly, NSA) 50 Day MA
2239 REALLN_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Monthly, SA)
2241 REALLN_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Monthly, SA)
2242 REALLN_Log Log of Real Estate Loans, All Commercial Banks (Monthly, SA)
2243 REALLN_mva200 Real Estate Loans, All Commercial Banks (Monthly, SA) 200 Day MA
2244 REALLN_mva050 Real Estate Loans, All Commercial Banks (Monthly, SA) 50 Day MA
2248 RELACBW027NBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Weekly, NSA)
2250 RELACBW027NBOG_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Weekly, NSA)
2251 RELACBW027NBOG_Log Log of Real Estate Loans, All Commercial Banks (Weekly, NSA)
2252 RELACBW027NBOG_mva200 Real Estate Loans, All Commercial Banks (Weekly, NSA) 200 Day MA
2253 RELACBW027NBOG_mva050 Real Estate Loans, All Commercial Banks (Weekly, NSA) 50 Day MA
2257 RELACBW027SBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans, All Commercial Banks (Weekly, SA)
2259 RELACBW027SBOG_SmoothDer Derivative of Smoothed Real Estate Loans, All Commercial Banks (Weekly, SA)
2260 RELACBW027SBOG_Log Log of Real Estate Loans, All Commercial Banks (Weekly, SA)
2261 RELACBW027SBOG_mva200 Real Estate Loans, All Commercial Banks (Weekly, SA) 200 Day MA
2262 RELACBW027SBOG_mva050 Real Estate Loans, All Commercial Banks (Weekly, SA) 50 Day MA
2263 RREACBM027NBOG_YoY Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) Year over Year
2265 RREACBM027NBOG_YoY5 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) 5 Year over 5 Year
2268 RREACBM027NBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2269 RREACBM027NBOG_Log Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA)
2270 RREACBM027NBOG_mva200 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) 200 Day MA
2271 RREACBM027NBOG_mva050 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, NSA) 50 Day MA
2275 RREACBM027SBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
2277 RREACBM027SBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
2278 RREACBM027SBOG_Log Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA)
2279 RREACBM027SBOG_mva200 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA) 200 Day MA
2280 RREACBM027SBOG_mva050 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Monthly, SA) 50 Day MA
2284 RREACBW027SBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2286 RREACBW027SBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2287 RREACBW027SBOG_Log Log of Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA)
2288 RREACBW027SBOG_mva200 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA) 200 Day MA
2289 RREACBW027SBOG_mva050 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, SA) 50 Day MA
2293 RREACBW027NBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA)
2295 RREACBW027NBOG_SmoothDer Derivative of Smoothed Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA)
2297 RREACBW027NBOG_mva200 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA) 200 Day MA
2298 RREACBW027NBOG_mva050 Real Estate Loans: Residential Real Estate Loans, All Commercial Banks (Weekly, NSA) 50 Day MA
2302 MORTGAGE30US_Smooth Savitsky-Golay Smoothed (p=3, n=365) 30-Year Fixed Rate Mortgage Average in the United States
2306 MORTGAGE30US_mva200 30-Year Fixed Rate Mortgage Average in the United States 200 Day MA
2307 MORTGAGE30US_mva050 30-Year Fixed Rate Mortgage Average in the United States 50 Day MA
2313 CONSUMERNSA_SmoothDer Derivative of Smoothed Consumer Loans, All Commercial Banks
2314 CONSUMERNSA_Log Log of Consumer Loans, All Commercial Banks
2315 CONSUMERNSA_mva200 Consumer Loans, All Commercial Banks 200 Day MA
2316 CONSUMERNSA_mva050 Consumer Loans, All Commercial Banks 50 Day MA
2320 TOTLLNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Loans and Leases in Bank Credit, All Commercial Banks
2322 TOTLLNSA_SmoothDer Derivative of Smoothed Loans and Leases in Bank Credit, All Commercial Banks
2323 TOTLLNSA_Log Log of Loans and Leases in Bank Credit, All Commercial Banks
2324 TOTLLNSA_mva200 Loans and Leases in Bank Credit, All Commercial Banks 200 Day MA
2325 TOTLLNSA_mva050 Loans and Leases in Bank Credit, All Commercial Banks 50 Day MA
2329 DPSACBW027SBOG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Deposits, All Commercial Banks
2332 DPSACBW027SBOG_Log Log of Deposits, All Commercial Banks
2333 DPSACBW027SBOG_mva200 Deposits, All Commercial Banks 200 Day MA
2334 DPSACBW027SBOG_mva050 Deposits, All Commercial Banks 50 Day MA
2335 DRCLACBS_YoY Delinquency Rate on Consumer Loans, All Commercial Banks, SA Year over Year
2336 DRCLACBS_YoY4 Delinquency Rate on Consumer Loans, All Commercial Banks, SA 4 Year over 4 Year
2337 DRCLACBS_YoY5 Delinquency Rate on Consumer Loans, All Commercial Banks, SA 5 Year over 5 Year
2340 DRCLACBS_SmoothDer Derivative of Smoothed Delinquency Rate on Consumer Loans, All Commercial Banks, SA
2341 DRCLACBS_Log Log of Delinquency Rate on Consumer Loans, All Commercial Banks, SA
2343 DRCLACBS_mva050 Delinquency Rate on Consumer Loans, All Commercial Banks, SA 50 Day MA
2344 TOTCINSA_YoY Commercial and Industrial Loans, All Commercial Banks (Weekly, NSA) Year over Year
2346 TOTCINSA_YoY5 Commercial and Industrial Loans, All Commercial Banks (Weekly, NSA) 5 Year over 5 Year
2347 TOTCINSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Commercial and Industrial Loans, All Commercial Banks (Weekly, NSA)
2349 TOTCINSA_SmoothDer Derivative of Smoothed Commercial and Industrial Loans, All Commercial Banks (Weekly, NSA)
2350 TOTCINSA_Log Log of Commercial and Industrial Loans, All Commercial Banks (Weekly, NSA)
2353 SRPSABSNNCB_YoY Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Year over Year
2354 SRPSABSNNCB_YoY4 Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 4 Year over 4 Year
2355 SRPSABSNNCB_YoY5 Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) 5 Year over 5 Year
2356 SRPSABSNNCB_Smooth Savitsky-Golay Smoothed (p=3, n=365) Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
2359 SRPSABSNNCB_Log Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA)
2368 ASTLL_Log Log of All sectors; total loans; liability, Level (NSA)
2369 ASTLL_mva200 All sectors; total loans; liability, Level (NSA) 200 Day MA
2370 ASTLL_mva050 All sectors; total loans; liability, Level (NSA) 50 Day MA
2376 FBDILNECA_SmoothDer Derivative of Smoothed Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
2377 FBDILNECA_Log Log of Domestic financial sectors; depository institution loans n.e.c.; asset, Level (NSA)
2386 ASOLAL_Log Log of All sectors; other loans and advances; liability, Level (NSA)
2387 ASOLAL_mva200 All sectors; other loans and advances; liability, Level (NSA) 200 Day MA
2388 ASOLAL_mva050 All sectors; other loans and advances; liability, Level (NSA) 50 Day MA
2395 ASTMA_Log Log of All sectors; total mortgages; asset, Level (NSA)
2396 ASTMA_mva200 All sectors; total mortgages; asset, Level (NSA) 200 Day MA
2397 ASTMA_mva050 All sectors; total mortgages; asset, Level (NSA) 50 Day MA
2404 ASHMA_Log Log of All sectors; home mortgages; asset, Level (NSA)
2405 ASHMA_mva200 All sectors; home mortgages; asset, Level (NSA) 200 Day MA
2406 ASHMA_mva050 All sectors; home mortgages; asset, Level (NSA) 50 Day MA
2413 ASMRMA_Log Log of All sectors; multifamily residential mortgages; asset, Level (NSA)
2414 ASMRMA_mva200 All sectors; multifamily residential mortgages; asset, Level (NSA) 200 Day MA
2415 ASMRMA_mva050 All sectors; multifamily residential mortgages; asset, Level (NSA) 50 Day MA
2422 ASCMA_Log Log of All sectors; commercial mortgages; asset, Level (NSA)
2423 ASCMA_mva200 All sectors; commercial mortgages; asset, Level (NSA) 200 Day MA
2424 ASCMA_mva050 All sectors; commercial mortgages; asset, Level (NSA) 50 Day MA
2431 ASFMA_Log Log of All sectors; farm mortgages; asset, Level (NSA)
2432 ASFMA_mva200 All sectors; farm mortgages; asset, Level (NSA) 200 Day MA
2433 ASFMA_mva050 All sectors; farm mortgages; asset, Level (NSA) 50 Day MA
2440 CCLBSHNO_Log Log of Households and nonprofit organizations; consumer credit; liability, Level (NSA)
2441 CCLBSHNO_mva200 Households and nonprofit organizations; consumer credit; liability, Level (NSA) 200 Day MA
2442 CCLBSHNO_mva050 Households and nonprofit organizations; consumer credit; liability, Level (NSA) 50 Day MA
2449 FBDSILQ027S_Log Log of Domestic financial sectors debt securities; liability, Level (NSA)
2450 FBDSILQ027S_mva200 Domestic financial sectors debt securities; liability, Level (NSA) 200 Day MA
2451 FBDSILQ027S_mva050 Domestic financial sectors debt securities; liability, Level (NSA) 50 Day MA
2453 FBLL_YoY4 Domestic financial sectors loans; liability, Level (NSA) 4 Year over 4 Year
2454 FBLL_YoY5 Domestic financial sectors loans; liability, Level (NSA) 5 Year over 5 Year
2455 FBLL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Domestic financial sectors loans; liability, Level (NSA)
2458 FBLL_Log Log of Domestic financial sectors loans; liability, Level (NSA)
2459 FBLL_mva200 Domestic financial sectors loans; liability, Level (NSA) 200 Day MA
2460 FBLL_mva050 Domestic financial sectors loans; liability, Level (NSA) 50 Day MA
2467 NCBDBIQ027S_Log Log of Nonfinancial corporate business; debt securities; liability, Level
2468 NCBDBIQ027S_mva200 Nonfinancial corporate business; debt securities; liability, Level 200 Day MA
2469 NCBDBIQ027S_mva050 Nonfinancial corporate business; debt securities; liability, Level 50 Day MA
2473 DGS10_Smooth Savitsky-Golay Smoothed (p=3, n=365) 10-Year Treasury Constant Maturity Rate
2482 TNX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2491 TNX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2500 TNX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2509 TNX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2515 TNX.Volume_YoY Year over Year
2516 TNX.Volume_YoY4 4 Year over 4 Year
2517 TNX.Volume_YoY5 5 Year over 5 Year
2518 TNX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2519 TNX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2520 TNX.Volume_SmoothDer Derivative of Smoothed
2521 TNX.Volume_Log Log of
2522 TNX.Volume_mva200 200 Day MA
2523 TNX.Volume_mva050 50 Day MA
2527 TNX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2539 CLF.Open_Log Log of
2540 CLF.Open_mva200 200 Day MA
2549 CLF.High_mva200 200 Day MA
2557 CLF.Low_Log Log of
2558 CLF.Low_mva200 200 Day MA
2566 CLF.Close_Log Log of
2567 CLF.Close_mva200 200 Day MA
2575 CLF.Volume_Log Log of
2584 CLF.Adjusted_Log Log of
2585 CLF.Adjusted_mva200 200 Day MA
2596 DGS1_YoY 1-Year Treasury Constant Maturity Rate Year over Year
2599 DGS1_Smooth Savitsky-Golay Smoothed (p=3, n=365) 1-Year Treasury Constant Maturity Rate
2600 DGS1_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) 1-Year Treasury Constant Maturity Rate
2601 DGS1_SmoothDer Derivative of Smoothed 1-Year Treasury Constant Maturity Rate
2603 DGS1_mva200 1-Year Treasury Constant Maturity Rate 200 Day MA
2604 DGS1_mva050 1-Year Treasury Constant Maturity Rate 50 Day MA
2608 DGS2_Smooth Savitsky-Golay Smoothed (p=3, n=365) 2-Year Treasury Constant Maturity Rate
2610 DGS2_SmoothDer Derivative of Smoothed 2-Year Treasury Constant Maturity Rate
2612 DGS2_mva200 2-Year Treasury Constant Maturity Rate 200 Day MA
2613 DGS2_mva050 2-Year Treasury Constant Maturity Rate 50 Day MA
2614 TB3MS_YoY 3-Month Treasury Bill: Secondary Market Rate (Monthly) Year over Year
2619 TB3MS_SmoothDer Derivative of Smoothed 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2620 TB3MS_Log Log of 3-Month Treasury Bill: Secondary Market Rate (Monthly)
2621 TB3MS_mva200 3-Month Treasury Bill: Secondary Market Rate (Monthly) 200 Day MA
2622 TB3MS_mva050 3-Month Treasury Bill: Secondary Market Rate (Monthly) 50 Day MA
2628 DTB3_SmoothDer Derivative of Smoothed 3-Month Treasury Bill: Secondary Market Rate (Daily)
2629 DTB3_Log Log of 3-Month Treasury Bill: Secondary Market Rate (Daily)
2630 DTB3_mva200 3-Month Treasury Bill: Secondary Market Rate (Daily) 200 Day MA
2631 DTB3_mva050 3-Month Treasury Bill: Secondary Market Rate (Daily) 50 Day MA
2637 IRX.Open_SmoothDer Derivative of Smoothed
2638 IRX.Open_Log Log of
2639 IRX.Open_mva200 200 Day MA
2646 IRX.High_SmoothDer Derivative of Smoothed
2647 IRX.High_Log Log of
2648 IRX.High_mva200 200 Day MA
2655 IRX.Low_SmoothDer Derivative of Smoothed
2656 IRX.Low_Log Log of
2657 IRX.Low_mva200 200 Day MA
2664 IRX.Close_SmoothDer Derivative of Smoothed
2665 IRX.Close_Log Log of
2666 IRX.Close_mva200 200 Day MA
2668 IRX.Volume_YoY Year over Year
2669 IRX.Volume_YoY4 4 Year over 4 Year
2670 IRX.Volume_YoY5 5 Year over 5 Year
2671 IRX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2672 IRX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2673 IRX.Volume_SmoothDer Derivative of Smoothed
2674 IRX.Volume_Log Log of
2675 IRX.Volume_mva200 200 Day MA
2676 IRX.Volume_mva050 50 Day MA
2682 IRX.Adjusted_SmoothDer Derivative of Smoothed
2683 IRX.Adjusted_Log Log of
2684 IRX.Adjusted_mva200 200 Day MA
2692 DCOILWTICO_Log Log of Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma
2693 DCOILWTICO_mva200 Crude Oil Prices: West Texas Intermediate (WTI) Cushing, Oklahoma 200 Day MA
2702 DCOILBRENTEU_mva200 Crude Oil Prices: Brent - Europe 200 Day MA
2711 NEWORDER_mva200 Manufacturers’ New Orders: Nondefense Capital Goods Excluding Aircraft 200 Day MA
2718 ALTSALES_SmoothDer Derivative of Smoothed Light Weight Vehicle Sales: Autos and Light Trucks
2727 ICSA_SmoothDer Derivative of Smoothed Initial Jobless Claims
2734 GSPC.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2738 GSPC.Open_mva200 200 Day MA
2739 GSPC.Open_mva050 50 Day MA
2743 GSPC.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2747 GSPC.High_mva200 200 Day MA
2748 GSPC.High_mva050 50 Day MA
2752 GSPC.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2756 GSPC.Low_mva200 200 Day MA
2757 GSPC.Low_mva050 50 Day MA
2761 GSPC.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2765 GSPC.Close_mva200 200 Day MA
2766 GSPC.Close_mva050 50 Day MA
2772 GSPC.Volume_SmoothDer Derivative of Smoothed
2779 GSPC.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2783 GSPC.Adjusted_mva200 200 Day MA
2784 GSPC.Adjusted_mva050 50 Day MA
2788 RLG.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2792 RLG.Open_mva200 200 Day MA
2793 RLG.Open_mva050 50 Day MA
2797 RLG.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2801 RLG.High_mva200 200 Day MA
2802 RLG.High_mva050 50 Day MA
2806 RLG.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2810 RLG.Low_mva200 200 Day MA
2811 RLG.Low_mva050 50 Day MA
2815 RLG.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2819 RLG.Close_mva200 200 Day MA
2820 RLG.Close_mva050 50 Day MA
2821 RLG.Volume_YoY Year over Year
2822 RLG.Volume_YoY4 4 Year over 4 Year
2823 RLG.Volume_YoY5 5 Year over 5 Year
2824 RLG.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2825 RLG.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
2826 RLG.Volume_SmoothDer Derivative of Smoothed
2827 RLG.Volume_Log Log of
2828 RLG.Volume_mva200 200 Day MA
2829 RLG.Volume_mva050 50 Day MA
2833 RLG.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2837 RLG.Adjusted_mva200 200 Day MA
2838 RLG.Adjusted_mva050 50 Day MA
2842 DJI.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2846 DJI.Open_mva200 200 Day MA
2851 DJI.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2855 DJI.High_mva200 200 Day MA
2860 DJI.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2864 DJI.Low_mva200 200 Day MA
2869 DJI.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2873 DJI.Close_mva200 200 Day MA
2878 DJI.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2880 DJI.Volume_SmoothDer Derivative of Smoothed
2887 DJI.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2891 DJI.Adjusted_mva200 200 Day MA
2896 STOXX50E.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2900 STOXX50E.Open_mva200 200 Day MA
2905 STOXX50E.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2909 STOXX50E.High_mva200 200 Day MA
2914 STOXX50E.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2918 STOXX50E.Low_mva200 200 Day MA
2923 STOXX50E.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2927 STOXX50E.Close_mva200 200 Day MA
2932 STOXX50E.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2934 STOXX50E.Volume_SmoothDer Derivative of Smoothed
2935 STOXX50E.Volume_Log Log of
2941 STOXX50E.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2945 STOXX50E.Adjusted_mva200 200 Day MA
2986 EFA.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
2988 EFA.Volume_SmoothDer Derivative of Smoothed
2990 EFA.Volume_mva200 200 Day MA
2991 EFA.Volume_mva050 50 Day MA
3007 GDP_Log Log of Gross Domestic Product
3008 GDP_mva200 Gross Domestic Product 200 Day MA
3009 GDP_mva050 Gross Domestic Product 50 Day MA
3015 FNDEFX_SmoothDer Derivative of Smoothed Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3016 FNDEFX_Log Log of Federal Government: Nondefense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3021 FDEFX_YoY5 Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 5 Year over 5 Year
3025 FDEFX_Log Log of Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate)
3026 FDEFX_mva200 Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 200 Day MA
3027 FDEFX_mva050 Federal Government: National Defense Consumption Expenditures and Gross Investment (SA, Annual Rate) 50 Day MA
3029 GDPNOW_YoY4 Fed Atlanta GDPNow 4 Year over 4 Year
3030 GDPNOW_YoY5 Fed Atlanta GDPNow 5 Year over 5 Year
3031 GDPNOW_Smooth Savitsky-Golay Smoothed (p=3, n=365) Fed Atlanta GDPNow
3033 GDPNOW_SmoothDer Derivative of Smoothed Fed Atlanta GDPNow
3034 GDPNOW_Log Log of Fed Atlanta GDPNow
3035 GDPNOW_mva200 Fed Atlanta GDPNow 200 Day MA
3036 GDPNOW_mva050 Fed Atlanta GDPNow 50 Day MA
3043 GDPC1_Log Log of Real Gross Domestic Product
3044 GDPC1_mva200 Real Gross Domestic Product 200 Day MA
3045 GDPC1_mva050 Real Gross Domestic Product 50 Day MA
3052 GDPDEF_Log Log of Gross Domestic Product: Implicit Price Deflator
3053 GDPDEF_mva200 Gross Domestic Product: Implicit Price Deflator 200 Day MA
3054 GDPDEF_mva050 Gross Domestic Product: Implicit Price Deflator 50 Day MA
3058 VIG.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3062 VIG.Open_mva200 200 Day MA
3063 VIG.Open_mva050 50 Day MA
3067 VIG.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3071 VIG.High_mva200 200 Day MA
3072 VIG.High_mva050 50 Day MA
3076 VIG.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3080 VIG.Low_mva200 200 Day MA
3081 VIG.Low_mva050 50 Day MA
3085 VIG.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3089 VIG.Close_mva200 200 Day MA
3090 VIG.Close_mva050 50 Day MA
3094 VIG.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3096 VIG.Volume_SmoothDer Derivative of Smoothed
3103 VIG.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3107 VIG.Adjusted_mva200 200 Day MA
3108 VIG.Adjusted_mva050 50 Day MA
3116 WLRRAL_mva200 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 200 Day MA
3117 WLRRAL_mva050 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) 50 Day MA
3133 GPDI_Log Log of Gross Private Domestic Investment
3134 GPDI_mva200 Gross Private Domestic Investment 200 Day MA
3135 GPDI_mva050 Gross Private Domestic Investment 50 Day MA
3142 W790RC1Q027SBEA_Log Log of Net domestic investment: Private: Domestic busines
3143 W790RC1Q027SBEA_mva200 Net domestic investment: Private: Domestic busines 200 Day MA
3144 W790RC1Q027SBEA_mva050 Net domestic investment: Private: Domestic busines 50 Day MA
3145 MZMV_YoY Velocity of MZM Money Stock Year over Year
3147 MZMV_YoY5 Velocity of MZM Money Stock 5 Year over 5 Year
3151 MZMV_Log Log of Velocity of MZM Money Stock
3152 MZMV_mva200 Velocity of MZM Money Stock 200 Day MA
3153 MZMV_mva050 Velocity of MZM Money Stock 50 Day MA
3157 M1_Smooth Savitsky-Golay Smoothed (p=3, n=365) M1 Money Stock
3159 M1_SmoothDer Derivative of Smoothed M1 Money Stock
3160 M1_Log Log of M1 Money Stock
3162 M1_mva050 M1 Money Stock 50 Day MA
3166 M2_Smooth Savitsky-Golay Smoothed (p=3, n=365) M2 Money Stock
3168 M2_SmoothDer Derivative of Smoothed M2 Money Stock
3169 M2_Log Log of M2 Money Stock
3171 M2_mva050 M2 Money Stock 50 Day MA
3177 OPHNFB_SmoothDer Derivative of Smoothed Nonfarm Business Sector: Real Output Per Hour of All Persons
3178 OPHNFB_Log Log of Nonfarm Business Sector: Real Output Per Hour of All Persons
3182 IPMAN_YoY4 Industrial Production: Manufacturing (NAICS) 4 Year over 4 Year
3184 IPMAN_Smooth Savitsky-Golay Smoothed (p=3, n=365) Industrial Production: Manufacturing (NAICS)
3186 IPMAN_SmoothDer Derivative of Smoothed Industrial Production: Manufacturing (NAICS)
3187 IPMAN_Log Log of Industrial Production: Manufacturing (NAICS)
3188 IPMAN_mva200 Industrial Production: Manufacturing (NAICS) 200 Day MA
3189 IPMAN_mva050 Industrial Production: Manufacturing (NAICS) 50 Day MA
3193 IWD.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3197 IWD.Open_mva200 200 Day MA
3202 IWD.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3206 IWD.High_mva200 200 Day MA
3211 IWD.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3215 IWD.Low_mva200 200 Day MA
3220 IWD.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3224 IWD.Close_mva200 200 Day MA
3229 IWD.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3231 IWD.Volume_SmoothDer Derivative of Smoothed
3233 IWD.Volume_mva200 200 Day MA
3234 IWD.Volume_mva050 50 Day MA
3238 IWD.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3242 IWD.Adjusted_mva200 200 Day MA
3247 GS5_Smooth Savitsky-Golay Smoothed (p=3, n=365) 5-Year Treasury Constant Maturity Rate
3249 GS5_SmoothDer Derivative of Smoothed 5-Year Treasury Constant Maturity Rate
3250 GS5_Log Log of 5-Year Treasury Constant Maturity Rate
3251 GS5_mva200 5-Year Treasury Constant Maturity Rate 200 Day MA
3252 GS5_mva050 5-Year Treasury Constant Maturity Rate 50 Day MA
3258 PSAVERT_SmoothDer Derivative of Smoothed Personal Saving Rate
3265 VIXCLS_Smooth Savitsky-Golay Smoothed (p=3, n=365) CBOE Volatility Index
3267 VIXCLS_SmoothDer Derivative of Smoothed CBOE Volatility Index
3276 VXX.Open_SmoothDer Derivative of Smoothed
3285 VXX.High_SmoothDer Derivative of Smoothed
3294 VXX.Low_SmoothDer Derivative of Smoothed
3303 VXX.Close_SmoothDer Derivative of Smoothed
3308 VXX.Volume_YoY4 4 Year over 4 Year
3309 VXX.Volume_YoY5 5 Year over 5 Year
3310 VXX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3312 VXX.Volume_SmoothDer Derivative of Smoothed
3313 VXX.Volume_Log Log of
3314 VXX.Volume_mva200 200 Day MA
3321 VXX.Adjusted_SmoothDer Derivative of Smoothed
3328 HOUST1F_Smooth Savitsky-Golay Smoothed (p=3, n=365) Privately Owned Housing Starts: 1-Unit Structures
3330 HOUST1F_SmoothDer Derivative of Smoothed Privately Owned Housing Starts: 1-Unit Structures
3331 HOUST1F_Log Log of Privately Owned Housing Starts: 1-Unit Structures
3333 HOUST1F_mva050 Privately Owned Housing Starts: 1-Unit Structures 50 Day MA
3337 GFDEBTN_Smooth Savitsky-Golay Smoothed (p=3, n=365) Federal Debt: Total Public Debt
3339 GFDEBTN_SmoothDer Derivative of Smoothed Federal Debt: Total Public Debt
3340 GFDEBTN_Log Log of Federal Debt: Total Public Debt
3346 HOUST_Smooth Savitsky-Golay Smoothed (p=3, n=365) Housing Starts: Total: New Privately Owned Housing Units Started
3348 HOUST_SmoothDer Derivative of Smoothed Housing Starts: Total: New Privately Owned Housing Units Started
3349 HOUST_Log Log of Housing Starts: Total: New Privately Owned Housing Units Started
3350 HOUST_mva200 Housing Starts: Total: New Privately Owned Housing Units Started 200 Day MA
3351 HOUST_mva050 Housing Starts: Total: New Privately Owned Housing Units Started 50 Day MA
3358 MSPUS_Log Log of Median Sales Price of Houses Sold for the United States
3359 MSPUS_mva200 Median Sales Price of Houses Sold for the United States 200 Day MA
3360 MSPUS_mva050 Median Sales Price of Houses Sold for the United States 50 Day MA
3362 UMDMNO_YoY4 Manufacturers’ New Orders: Durable Goods (NSA) 4 Year over 4 Year
3363 UMDMNO_YoY5 Manufacturers’ New Orders: Durable Goods (NSA) 5 Year over 5 Year
3371 DGORDER_YoY4 Manufacturers’ New Orders: Durable Goods (SA) 4 Year over 4 Year
3373 DGORDER_Smooth Savitsky-Golay Smoothed (p=3, n=365) Manufacturers’ New Orders: Durable Goods (SA)
3376 DGORDER_Log Log of Manufacturers’ New Orders: Durable Goods (SA)
3377 DGORDER_mva200 Manufacturers’ New Orders: Durable Goods (SA) 200 Day MA
3378 DGORDER_mva050 Manufacturers’ New Orders: Durable Goods (SA) 50 Day MA
3385 CSUSHPINSA_Log Log of S&P/Case-Shiller U.S. National Home Price Index (NSA)
3386 CSUSHPINSA_mva200 S&P/Case-Shiller U.S. National Home Price Index (NSA) 200 Day MA
3387 CSUSHPINSA_mva050 S&P/Case-Shiller U.S. National Home Price Index (NSA) 50 Day MA
3390 GFDEGDQ188S_YoY5 Federal Debt: Total Public Debt as Percent of Gross Domestic Product 5 Year over 5 Year
3393 GFDEGDQ188S_SmoothDer Derivative of Smoothed Federal Debt: Total Public Debt as Percent of Gross Domestic Product
3394 GFDEGDQ188S_Log Log of Federal Debt: Total Public Debt as Percent of Gross Domestic Product
3403 FYFSD_Log Log of Federal Surplus or Deficit
3404 FYFSD_mva200 Federal Surplus or Deficit 200 Day MA
3405 FYFSD_mva050 Federal Surplus or Deficit 50 Day MA
3406 FYFSGDA188S_YoY Federal Surplus or Deficit [-] as Percent of Gross Domestic Product Year over Year
3412 FYFSGDA188S_Log Log of Federal Surplus or Deficit [-] as Percent of Gross Domestic Product
3413 FYFSGDA188S_mva200 Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 200 Day MA
3414 FYFSGDA188S_mva050 Federal Surplus or Deficit [-] as Percent of Gross Domestic Product 50 Day MA
3481 XLE.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3490 XLE.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3499 XLE.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3508 XLE.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3519 XLE.Volume_SmoothDer Derivative of Smoothed
3526 XLE.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3530 XLE.Adjusted_mva200 200 Day MA
3539 GSG.Open_mva200 200 Day MA
3544 GSG.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3548 GSG.High_mva200 200 Day MA
3557 GSG.Low_mva200 200 Day MA
3562 GSG.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3566 GSG.Close_mva200 200 Day MA
3571 GSG.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3580 GSG.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
3584 GSG.Adjusted_mva200 200 Day MA
3589 WALCL_Smooth Savitsky-Golay Smoothed (p=3, n=365) All Federal Reserve Banks: Total Assets
3593 WALCL_mva200 All Federal Reserve Banks: Total Assets 200 Day MA
3594 WALCL_mva050 All Federal Reserve Banks: Total Assets 50 Day MA
3597 OUTMS_YoY5 Manufacturing Sector: Real Output 5 Year over 5 Year
3601 OUTMS_Log Log of Manufacturing Sector: Real Output
3602 OUTMS_mva200 Manufacturing Sector: Real Output 200 Day MA
3603 OUTMS_mva050 Manufacturing Sector: Real Output 50 Day MA
3610 MANEMP_Log Log of All Employees: Manufacturing
3611 MANEMP_mva200 All Employees: Manufacturing 200 Day MA
3612 MANEMP_mva050 All Employees: Manufacturing 50 Day MA
3615 PRS30006163_YoY5 Manufacturing Sector: Real Output Per Person 5 Year over 5 Year
3616 PRS30006163_Smooth Savitsky-Golay Smoothed (p=3, n=365) Manufacturing Sector: Real Output Per Person
3619 PRS30006163_Log Log of Manufacturing Sector: Real Output Per Person
3625 BAMLC0A3CA_Smooth Savitsky-Golay Smoothed (p=3, n=365) ICE BofAML US Corporate A Option-Adjusted Spread
3627 BAMLC0A3CA_SmoothDer Derivative of Smoothed ICE BofAML US Corporate A Option-Adjusted Spread
3630 BAMLC0A3CA_mva050 ICE BofAML US Corporate A Option-Adjusted Spread 50 Day MA
3634 AAA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Moody’s Seasoned Aaa Corporate Bond Yield
3641 SOFR_YoY4 Secured Overnight Financing Rate 4 Year over 4 Year
3642 SOFR_YoY5 Secured Overnight Financing Rate 5 Year over 5 Year
3645 SOFR_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate
3646 SOFR_Log Log of Secured Overnight Financing Rate
3647 SOFR_mva200 Secured Overnight Financing Rate 200 Day MA
3654 SOFRVOL_SmoothDer Derivative of Smoothed Secured Overnight Financing Volume
3659 SOFR99_YoY4 Secured Overnight Financing Rate: 99th Percentile 4 Year over 4 Year
3660 SOFR99_YoY5 Secured Overnight Financing Rate: 99th Percentile 5 Year over 5 Year
3664 SOFR99_Log Log of Secured Overnight Financing Rate: 99th Percentile
3672 SOFR75_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate: 75th Percentile
3674 SOFR75_mva200 Secured Overnight Financing Rate: 75th Percentile 200 Day MA
3681 SOFR25_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate: 25th Percentile
3682 SOFR25_Log Log of Secured Overnight Financing Rate: 25th Percentile
3683 SOFR25_mva200 Secured Overnight Financing Rate: 25th Percentile 200 Day MA
3690 SOFR1_SmoothDer Derivative of Smoothed Secured Overnight Financing Rate: 1st Percentile
3691 SOFR1_Log Log of Secured Overnight Financing Rate: 1st Percentile
3699 OBFR_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate
3701 OBFR_mva200 Overnight Bank Funding Rate 200 Day MA
3706 OBFR99_Smooth Savitsky-Golay Smoothed (p=3, n=365) Overnight Bank Funding Rate: 99th Percentile
3708 OBFR99_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate: 99th Percentile
3711 OBFR99_mva050 Overnight Bank Funding Rate: 99th Percentile 50 Day MA
3717 OBFR75_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate: 75th Percentile
3719 OBFR75_mva200 Overnight Bank Funding Rate: 75th Percentile 200 Day MA
3721 OBFR25_YoY Overnight Bank Funding Rate: 25th Percentile Year over Year
3726 OBFR25_SmoothDer Derivative of Smoothed Overnight Bank Funding Rate: 25th Percentile
3728 OBFR25_mva200 Overnight Bank Funding Rate: 25th Percentile 200 Day MA
3730 OBFR1_YoY Overnight Bank Funding Rate: 1st Percentile Year over Year
3734 OBFR1_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Overnight Bank Funding Rate: 1st Percentile
3736 OBFR1_Log Log of Overnight Bank Funding Rate: 1st Percentile
3737 OBFR1_mva200 Overnight Bank Funding Rate: 1st Percentile 200 Day MA
3739 RPONTSYD_YoY Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations Year over Year
3742 RPONTSYD_Smooth Savitsky-Golay Smoothed (p=3, n=365) Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
3743 RPONTSYD_Smooth.short Savitsky-Golay Smoothed (p=3, n=15) Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
3745 RPONTSYD_Log Log of Overnight Repurchase Agreements: Treasury Securities Purchased by the Federal Reserve in the Temporary Open Market Operations
3748 IOER_YoY Interest Rate on Excess Reserves Year over Year
3754 IOER_Log Log of Interest Rate on Excess Reserves
3755 IOER_mva200 Interest Rate on Excess Reserves 200 Day MA
3756 IOER_mva050 Interest Rate on Excess Reserves 50 Day MA
3758 WRESBAL_YoY4 Reserve Balances with Federal Reserve Banks 4 Year over 4 Year
3759 WRESBAL_YoY5 Reserve Balances with Federal Reserve Banks 5 Year over 5 Year
3760 WRESBAL_Smooth Savitsky-Golay Smoothed (p=3, n=365) Reserve Balances with Federal Reserve Banks
3764 WRESBAL_mva200 Reserve Balances with Federal Reserve Banks 200 Day MA
3767 EXCSRESNW_YoY4 Excess Reserves of Depository Institutions 4 Year over 4 Year
3768 EXCSRESNW_YoY5 Excess Reserves of Depository Institutions 5 Year over 5 Year
3771 EXCSRESNW_SmoothDer Derivative of Smoothed Excess Reserves of Depository Institutions
3772 EXCSRESNW_Log Log of Excess Reserves of Depository Institutions
3773 EXCSRESNW_mva200 Excess Reserves of Depository Institutions 200 Day MA
3774 EXCSRESNW_mva050 Excess Reserves of Depository Institutions 50 Day MA
3775 ECBASSETS_YoY Central Bank Assets for Euro Area (11-19 Countries) Year over Year
3781 ECBASSETS_Log Log of Central Bank Assets for Euro Area (11-19 Countries)
3782 ECBASSETS_mva200 Central Bank Assets for Euro Area (11-19 Countries) 200 Day MA
3783 ECBASSETS_mva050 Central Bank Assets for Euro Area (11-19 Countries) 50 Day MA
3790 EUNNGDP_Log Log of Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries)
3791 EUNNGDP_mva200 Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 200 Day MA
3792 EUNNGDP_mva050 Gross Domestic Product (Euro/ECU series) for Euro Area (19 Countries) 50 Day MA
3794 CEU0600000007_YoY4 Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing 4 Year over 4 Year
3795 CEU0600000007_YoY5 Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing 5 Year over 5 Year
3796 CEU0600000007_Smooth Savitsky-Golay Smoothed (p=3, n=365) Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3798 CEU0600000007_SmoothDer Derivative of Smoothed Average Weekly Hours of Production and Nonsupervisory Employees: Goods-Producing
3805 USD1MTD156N_Smooth Savitsky-Golay Smoothed (p=3, n=365) 1-Month London Interbank Offered Rate (LIBOR), based on U.S. Dollar
3807 USD1MTD156N_SmoothDer Derivative of Smoothed 1-Month London Interbank Offered Rate (LIBOR), based on U.S. Dollar
3810 USD1MTD156N_mva050 1-Month London Interbank Offered Rate (LIBOR), based on U.S. Dollar 50 Day MA
3814 CURRENCY_Smooth Savitsky-Golay Smoothed (p=3, n=365) Currency Component of M1 (Seasonally Adjusted)
3816 CURRENCY_SmoothDer Derivative of Smoothed Currency Component of M1 (Seasonally Adjusted)
3817 CURRENCY_Log Log of Currency Component of M1 (Seasonally Adjusted)
3818 CURRENCY_mva200 Currency Component of M1 (Seasonally Adjusted) 200 Day MA
3819 CURRENCY_mva050 Currency Component of M1 (Seasonally Adjusted) 50 Day MA
3823 WCURRNS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Currency Component of M1
3826 WCURRNS_Log Log of Currency Component of M1
3827 WCURRNS_mva200 Currency Component of M1 200 Day MA
3828 WCURRNS_mva050 Currency Component of M1 50 Day MA
3830 BOGMBASE_YoY4 Monetary Base; Total 4 Year over 4 Year
3832 BOGMBASE_Smooth Savitsky-Golay Smoothed (p=3, n=365) Monetary Base; Total
3835 BOGMBASE_Log Log of Monetary Base; Total
3836 BOGMBASE_mva200 Monetary Base; Total 200 Day MA
3837 BOGMBASE_mva050 Monetary Base; Total 50 Day MA
3841 PRS88003193_Smooth Savitsky-Golay Smoothed (p=3, n=365) Nonfinancial Corporations Sector: Unit Profits
3844 PRS88003193_Log Log of Nonfinancial Corporations Sector: Unit Profits
3845 PRS88003193_mva200 Nonfinancial Corporations Sector: Unit Profits 200 Day MA
3846 PRS88003193_mva050 Nonfinancial Corporations Sector: Unit Profits 50 Day MA
3850 PPIACO_Smooth Savitsky-Golay Smoothed (p=3, n=365) Producer Price Index for All Commodities
3853 PPIACO_Log Log of Producer Price Index for All Commodities
3854 PPIACO_mva200 Producer Price Index for All Commodities 200 Day MA
3855 PPIACO_mva050 Producer Price Index for All Commodities 50 Day MA
3859 PCUOMFGOMFG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Producer Price Index by Industry: Total Manufacturing Industries
3862 PCUOMFGOMFG_Log Log of Producer Price Index by Industry: Total Manufacturing Industries
3863 PCUOMFGOMFG_mva200 Producer Price Index by Industry: Total Manufacturing Industries 200 Day MA
3864 PCUOMFGOMFG_mva050 Producer Price Index by Industry: Total Manufacturing Industries 50 Day MA
3875 POPTHM_SmoothDer Derivative of Smoothed Population (U.S.)
3876 POPTHM_SmoothDer Derivative of Smoothed Population (U.S.)
3877 POPTHM_Log Log of Population (U.S.)
3878 POPTHM_Log Log of Population (U.S.)
3879 POPTHM_mva200 Population (U.S.) 200 Day MA
3880 POPTHM_mva200 Population (U.S.) 200 Day MA
3881 POPTHM_mva050 Population (U.S.) 50 Day MA
3882 POPTHM_mva050 Population (U.S.) 50 Day MA
3893 POPTHM.1_SmoothDer Derivative of Smoothed
3894 POPTHM.1_SmoothDer Derivative of Smoothed
3895 POPTHM.1_Log Log of
3896 POPTHM.1_Log Log of
3897 POPTHM.1_mva200 200 Day MA
3898 POPTHM.1_mva200 200 Day MA
3899 POPTHM.1_mva050 50 Day MA
3900 POPTHM.1_mva050 50 Day MA
3901 CLF16OV_YoY Civilian Labor Force Level, SA Year over Year
3904 CLF16OV_Smooth Savitsky-Golay Smoothed (p=3, n=365) Civilian Labor Force Level, SA
3907 CLF16OV_Log Log of Civilian Labor Force Level, SA
3908 CLF16OV_mva200 Civilian Labor Force Level, SA 200 Day MA
3909 CLF16OV_mva050 Civilian Labor Force Level, SA 50 Day MA
3910 LNU01000000_YoY Civilian Labor Force Level, NSA Year over Year
3912 LNU01000000_YoY5 Civilian Labor Force Level, NSA 5 Year over 5 Year
3928 UNEMPLOY_YoY Unemployment Level, seasonally adjusted Year over Year
3940 RSAFS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Advance Retail Sales: Retail and Food Services
3943 RSAFS_Log Log of Advance Retail Sales: Retail and Food Services
3944 RSAFS_mva200 Advance Retail Sales: Retail and Food Services 200 Day MA
3945 RSAFS_mva050 Advance Retail Sales: Retail and Food Services 50 Day MA
3947 FRGSHPUSM649NCIS_YoY4 Cass Freight Index: Shipments 4 Year over 4 Year
3948 FRGSHPUSM649NCIS_YoY5 Cass Freight Index: Shipments 5 Year over 5 Year
3949 FRGSHPUSM649NCIS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Cass Freight Index: Shipments
3958 BOPGTB_Smooth Savitsky-Golay Smoothed (p=3, n=365) Trade Balance: Goods, Balance of Payments Basis (SA)
3960 BOPGTB_SmoothDer Derivative of Smoothed Trade Balance: Goods, Balance of Payments Basis (SA)
3961 BOPGTB_Log Log of Trade Balance: Goods, Balance of Payments Basis (SA)
3962 BOPGTB_mva200 Trade Balance: Goods, Balance of Payments Basis (SA) 200 Day MA
3963 BOPGTB_mva050 Trade Balance: Goods, Balance of Payments Basis (SA) 50 Day MA
3969 TERMCBPER24NS_SmoothDer Derivative of Smoothed Finance Rate on Personal Loans at Commercial Banks, 24 Month Loan
3973 A065RC1A027NBEA_YoY Personal income (NSA) Year over Year
3979 A065RC1A027NBEA_Log Log of Personal income (NSA)
3980 A065RC1A027NBEA_mva200 Personal income (NSA) 200 Day MA
3981 A065RC1A027NBEA_mva050 Personal income (NSA) 50 Day MA
3985 PI_Smooth Savitsky-Golay Smoothed (p=3, n=365) Personal income (SA)
3987 PI_SmoothDer Derivative of Smoothed Personal income (SA)
3990 PI_mva050 Personal income (SA) 50 Day MA
3994 PCE_Smooth Savitsky-Golay Smoothed (p=3, n=365) Personal Consumption Expenditures (SA)
3997 PCE_Log Log of Personal Consumption Expenditures (SA)
3998 PCE_mva200 Personal Consumption Expenditures (SA) 200 Day MA
3999 PCE_mva050 Personal Consumption Expenditures (SA) 50 Day MA
4003 A053RC1Q027SBEA_Smooth Savitsky-Golay Smoothed (p=3, n=365) National income: Corporate profits before tax (without IVA and CCAdj)
4006 A053RC1Q027SBEA_Log Log of National income: Corporate profits before tax (without IVA and CCAdj)
4007 A053RC1Q027SBEA_mva200 National income: Corporate profits before tax (without IVA and CCAdj) 200 Day MA
4008 A053RC1Q027SBEA_mva050 National income: Corporate profits before tax (without IVA and CCAdj) 50 Day MA
4015 CPROFIT_Log Log of Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj)
4016 CPROFIT_mva200 Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) 200 Day MA
4017 CPROFIT_mva050 Corporate Profits with Inventory Valuation Adjustment (IVA) and Capital Consumption Adjustment (CCAdj) 50 Day MA
4021 SPY.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4025 SPY.Open_mva200 200 Day MA
4026 SPY.Open_mva050 50 Day MA
4030 SPY.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4034 SPY.High_mva200 200 Day MA
4035 SPY.High_mva050 50 Day MA
4039 SPY.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4043 SPY.Low_mva200 200 Day MA
4044 SPY.Low_mva050 50 Day MA
4048 SPY.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4052 SPY.Close_mva200 200 Day MA
4053 SPY.Close_mva050 50 Day MA
4057 SPY.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4059 SPY.Volume_SmoothDer Derivative of Smoothed
4066 SPY.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4070 SPY.Adjusted_mva200 200 Day MA
4071 SPY.Adjusted_mva050 50 Day MA
4075 MDY.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4079 MDY.Open_mva200 200 Day MA
4084 MDY.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4088 MDY.High_mva200 200 Day MA
4093 MDY.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4097 MDY.Low_mva200 200 Day MA
4102 MDY.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4106 MDY.Close_mva200 200 Day MA
4111 MDY.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4113 MDY.Volume_SmoothDer Derivative of Smoothed
4116 MDY.Volume_mva050 50 Day MA
4120 MDY.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4124 MDY.Adjusted_mva200 200 Day MA
4129 EES.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4138 EES.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4142 EES.High_mva200 200 Day MA
4147 EES.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4156 EES.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4160 EES.Close_mva200 200 Day MA
4165 EES.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4167 EES.Volume_SmoothDer Derivative of Smoothed
4168 EES.Volume_Log Log of
4174 EES.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4178 EES.Adjusted_mva200 200 Day MA
4183 IJR.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4192 IJR.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4201 IJR.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4210 IJR.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4219 IJR.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4221 IJR.Volume_SmoothDer Derivative of Smoothed
4228 IJR.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4270 VGSTX.Volume_YoY Year over Year
4271 VGSTX.Volume_YoY4 4 Year over 4 Year
4272 VGSTX.Volume_YoY5 5 Year over 5 Year
4273 VGSTX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4274 VGSTX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4275 VGSTX.Volume_SmoothDer Derivative of Smoothed
4276 VGSTX.Volume_Log Log of
4277 VGSTX.Volume_mva200 200 Day MA
4278 VGSTX.Volume_mva050 50 Day MA
4291 VFINX.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4295 VFINX.Open_mva200 200 Day MA
4296 VFINX.Open_mva050 50 Day MA
4300 VFINX.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4304 VFINX.High_mva200 200 Day MA
4305 VFINX.High_mva050 50 Day MA
4309 VFINX.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4313 VFINX.Low_mva200 200 Day MA
4314 VFINX.Low_mva050 50 Day MA
4318 VFINX.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4322 VFINX.Close_mva200 200 Day MA
4323 VFINX.Close_mva050 50 Day MA
4324 VFINX.Volume_YoY Year over Year
4325 VFINX.Volume_YoY4 4 Year over 4 Year
4326 VFINX.Volume_YoY5 5 Year over 5 Year
4327 VFINX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4328 VFINX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4329 VFINX.Volume_SmoothDer Derivative of Smoothed
4330 VFINX.Volume_Log Log of
4331 VFINX.Volume_mva200 200 Day MA
4332 VFINX.Volume_mva050 50 Day MA
4336 VFINX.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4340 VFINX.Adjusted_mva200 200 Day MA
4341 VFINX.Adjusted_mva050 50 Day MA
4345 VOE.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4349 VOE.Open_mva200 200 Day MA
4354 VOE.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4358 VOE.High_mva200 200 Day MA
4363 VOE.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4367 VOE.Low_mva200 200 Day MA
4372 VOE.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4376 VOE.Close_mva200 200 Day MA
4381 VOE.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4383 VOE.Volume_SmoothDer Derivative of Smoothed
4390 VOE.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4393 VOE.Adjusted_Log Log of
4394 VOE.Adjusted_mva200 200 Day MA
4395 VOE.Adjusted_mva050 50 Day MA
4401 VOT.Open_SmoothDer Derivative of Smoothed
4403 VOT.Open_mva200 200 Day MA
4410 VOT.High_SmoothDer Derivative of Smoothed
4412 VOT.High_mva200 200 Day MA
4419 VOT.Low_SmoothDer Derivative of Smoothed
4421 VOT.Low_mva200 200 Day MA
4428 VOT.Close_SmoothDer Derivative of Smoothed
4430 VOT.Close_mva200 200 Day MA
4435 VOT.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4437 VOT.Volume_SmoothDer Derivative of Smoothed
4446 VOT.Adjusted_SmoothDer Derivative of Smoothed
4448 VOT.Adjusted_mva200 200 Day MA
4486 TMFGX.Volume_YoY Year over Year
4487 TMFGX.Volume_YoY4 4 Year over 4 Year
4488 TMFGX.Volume_YoY5 5 Year over 5 Year
4489 TMFGX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4490 TMFGX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4491 TMFGX.Volume_SmoothDer Derivative of Smoothed
4492 TMFGX.Volume_Log Log of
4493 TMFGX.Volume_mva200 200 Day MA
4494 TMFGX.Volume_mva050 50 Day MA
4507 IWM.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4516 IWM.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4534 IWM.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4543 IWM.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4545 IWM.Volume_SmoothDer Derivative of Smoothed
4547 IWM.Volume_mva200 200 Day MA
4552 IWM.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4561 ONEQ.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4563 ONEQ.Open_SmoothDer Derivative of Smoothed
4565 ONEQ.Open_mva200 200 Day MA
4570 ONEQ.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4572 ONEQ.High_SmoothDer Derivative of Smoothed
4574 ONEQ.High_mva200 200 Day MA
4579 ONEQ.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4583 ONEQ.Low_mva200 200 Day MA
4588 ONEQ.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4590 ONEQ.Close_SmoothDer Derivative of Smoothed
4592 ONEQ.Close_mva200 200 Day MA
4597 ONEQ.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4599 ONEQ.Volume_SmoothDer Derivative of Smoothed
4606 ONEQ.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4608 ONEQ.Adjusted_SmoothDer Derivative of Smoothed
4610 ONEQ.Adjusted_mva200 200 Day MA
4648 HAINX.Volume_YoY Year over Year
4649 HAINX.Volume_YoY4 4 Year over 4 Year
4650 HAINX.Volume_YoY5 5 Year over 5 Year
4651 HAINX.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4652 HAINX.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4653 HAINX.Volume_SmoothDer Derivative of Smoothed
4654 HAINX.Volume_Log Log of
4655 HAINX.Volume_mva200 200 Day MA
4656 HAINX.Volume_mva050 50 Day MA
4705 VEU.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4707 VEU.Volume_SmoothDer Derivative of Smoothed
4710 VEU.Volume_mva050 50 Day MA
4725 BIL.Open_SmoothDer Derivative of Smoothed
4734 BIL.High_SmoothDer Derivative of Smoothed
4743 BIL.Low_SmoothDer Derivative of Smoothed
4752 BIL.Close_SmoothDer Derivative of Smoothed
4761 BIL.Volume_SmoothDer Derivative of Smoothed
4770 BIL.Adjusted_SmoothDer Derivative of Smoothed
4777 IVOO.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4781 IVOO.Open_mva200 200 Day MA
4786 IVOO.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4790 IVOO.High_mva200 200 Day MA
4795 IVOO.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4799 IVOO.Low_mva200 200 Day MA
4804 IVOO.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4808 IVOO.Close_mva200 200 Day MA
4813 IVOO.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4815 IVOO.Volume_SmoothDer Derivative of Smoothed
4816 IVOO.Volume_Log Log of
4822 IVOO.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4826 IVOO.Adjusted_mva200 200 Day MA
4831 VO.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4835 VO.Open_mva200 200 Day MA
4840 VO.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4844 VO.High_mva200 200 Day MA
4849 VO.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4853 VO.Low_mva200 200 Day MA
4858 VO.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4862 VO.Close_mva200 200 Day MA
4867 VO.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4869 VO.Volume_SmoothDer Derivative of Smoothed
4870 VO.Volume_Log Log of
4871 VO.Volume_mva200 200 Day MA
4876 VO.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4880 VO.Adjusted_mva200 200 Day MA
4886 CZA.Open_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
4889 CZA.Open_mva200 200 Day MA
4894 CZA.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4897 CZA.High_Log Log of
4898 CZA.High_mva200 200 Day MA
4906 CZA.Low_Log Log of
4907 CZA.Low_mva200 200 Day MA
4912 CZA.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4915 CZA.Close_Log Log of
4916 CZA.Close_mva200 200 Day MA
4923 CZA.Volume_SmoothDer Derivative of Smoothed
4924 CZA.Volume_Log Log of
4930 CZA.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4933 CZA.Adjusted_Log Log of
4934 CZA.Adjusted_mva200 200 Day MA
4935 CZA.Adjusted_mva050 50 Day MA
4939 VYM.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4943 VYM.Open_mva200 200 Day MA
4944 VYM.Open_mva050 50 Day MA
4948 VYM.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4952 VYM.High_mva200 200 Day MA
4953 VYM.High_mva050 50 Day MA
4957 VYM.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4961 VYM.Low_mva200 200 Day MA
4962 VYM.Low_mva050 50 Day MA
4966 VYM.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4970 VYM.Close_mva200 200 Day MA
4971 VYM.Close_mva050 50 Day MA
4975 VYM.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4977 VYM.Volume_SmoothDer Derivative of Smoothed
4980 VYM.Volume_mva050 50 Day MA
4984 VYM.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
4988 VYM.Adjusted_mva200 200 Day MA
4989 VYM.Adjusted_mva050 50 Day MA
4997 ACWI.Open_mva200 200 Day MA
5002 ACWI.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5006 ACWI.High_mva200 200 Day MA
5015 ACWI.Low_mva200 200 Day MA
5020 ACWI.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5024 ACWI.Close_mva200 200 Day MA
5029 ACWI.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5031 ACWI.Volume_SmoothDer Derivative of Smoothed
5034 ACWI.Volume_mva050 50 Day MA
5038 ACWI.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5042 ACWI.Adjusted_mva200 200 Day MA
5047 SLY.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5056 SLY.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5065 SLY.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5074 SLY.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5080 SLY.Volume_YoY Year over Year
5083 SLY.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5084 SLY.Volume_Smooth.short Savitsky-Golay Smoothed (p=3, n=15)
5085 SLY.Volume_SmoothDer Derivative of Smoothed
5086 SLY.Volume_Log Log of
5088 SLY.Volume_mva050 50 Day MA
5092 SLY.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5101 QQQ.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5103 QQQ.Open_SmoothDer Derivative of Smoothed
5105 QQQ.Open_mva200 200 Day MA
5106 QQQ.Open_mva050 50 Day MA
5110 QQQ.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5112 QQQ.High_SmoothDer Derivative of Smoothed
5114 QQQ.High_mva200 200 Day MA
5115 QQQ.High_mva050 50 Day MA
5119 QQQ.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5123 QQQ.Low_mva200 200 Day MA
5124 QQQ.Low_mva050 50 Day MA
5128 QQQ.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5130 QQQ.Close_SmoothDer Derivative of Smoothed
5132 QQQ.Close_mva200 200 Day MA
5133 QQQ.Close_mva050 50 Day MA
5137 QQQ.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5139 QQQ.Volume_SmoothDer Derivative of Smoothed
5146 QQQ.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5148 QQQ.Adjusted_SmoothDer Derivative of Smoothed
5150 QQQ.Adjusted_mva200 200 Day MA
5151 QQQ.Adjusted_mva050 50 Day MA
5194 HYMB.Volume_Log Log of
5204 HYMB.Adjusted_mva200 200 Day MA
5212 GOLD.Open_Log Log of
5247 GOLD.Volume_SmoothDer Derivative of Smoothed
5248 GOLD.Volume_Log Log of
5263 BKR.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5265 BKR.Open_SmoothDer Derivative of Smoothed
5266 BKR.Open_Log Log of
5272 BKR.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5274 BKR.High_SmoothDer Derivative of Smoothed
5281 BKR.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5283 BKR.Low_SmoothDer Derivative of Smoothed
5290 BKR.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5292 BKR.Close_SmoothDer Derivative of Smoothed
5302 BKR.Volume_Log Log of
5308 BKR.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5310 BKR.Adjusted_SmoothDer Derivative of Smoothed
5317 SLB.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5326 SLB.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5335 SLB.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5344 SLB.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5355 SLB.Volume_SmoothDer Derivative of Smoothed
5362 SLB.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5371 HAL.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5374 HAL.Open_Log Log of
5380 HAL.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5389 HAL.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5398 HAL.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5409 HAL.Volume_SmoothDer Derivative of Smoothed
5410 HAL.Volume_Log Log of
5416 HAL.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5428 IP.Open_Log Log of
5461 IP.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5463 IP.Volume_SmoothDer Derivative of Smoothed
5515 PKG.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5517 PKG.Volume_SmoothDer Derivative of Smoothed
5533 UPS.Open_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5542 UPS.High_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5551 UPS.Low_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5560 UPS.Close_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5571 UPS.Volume_SmoothDer Derivative of Smoothed
5578 UPS.Adjusted_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5625 FDX.Volume_SmoothDer Derivative of Smoothed
5644 T.Open_Log Log of
5677 T.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5679 T.Volume_SmoothDer Derivative of Smoothed
5731 VZ.Volume_Smooth Savitsky-Golay Smoothed (p=3, n=365)
5733 VZ.Volume_SmoothDer Derivative of Smoothed
5749 ISMMANPMI_Smooth Savitsky-Golay Smoothed (p=3, n=365) Institute of Supply Managment PMI Composite Index
5751 ISMMANPMI_SmoothDer Derivative of Smoothed Institute of Supply Managment PMI Composite Index
5752 ISMMANPMI_Log Log of Institute of Supply Managment PMI Composite Index
5754 ISMMANPMI_mva050 Institute of Supply Managment PMI Composite Index 50 Day MA
5760 MULTPLSP500PERATIOMONTH_SmoothDer Derivative of Smoothed S&P 500 TTM P/E
5764 MULTPLSP500SALESQUARTER_YoY S&P 500 TTM Sales (Not Inflation Adjusted) Year over Year
5770 MULTPLSP500SALESQUARTER_Log Log of S&P 500 TTM Sales (Not Inflation Adjusted)
5771 MULTPLSP500SALESQUARTER_mva200 S&P 500 TTM Sales (Not Inflation Adjusted) 200 Day MA
5772 MULTPLSP500SALESQUARTER_mva050 S&P 500 TTM Sales (Not Inflation Adjusted) 50 Day MA
5773 MULTPLSP500DIVYIELDMONTH_YoY S&P 500 Dividend Yield by Month Year over Year
5774 MULTPLSP500DIVYIELDMONTH_YoY4 S&P 500 Dividend Yield by Month 4 Year over 4 Year
5775 MULTPLSP500DIVYIELDMONTH_YoY5 S&P 500 Dividend Yield by Month 5 Year over 5 Year
5778 MULTPLSP500DIVYIELDMONTH_SmoothDer Derivative of Smoothed S&P 500 Dividend Yield by Month
5782 MULTPLSP500DIVMONTH_YoY S&P 500 Dividend by Month (Inflation Adjusted) Year over Year
5785 MULTPLSP500DIVMONTH_Smooth Savitsky-Golay Smoothed (p=3, n=365) S&P 500 Dividend by Month (Inflation Adjusted)
5787 MULTPLSP500DIVMONTH_SmoothDer Derivative of Smoothed S&P 500 Dividend by Month (Inflation Adjusted)
5788 MULTPLSP500DIVMONTH_Log Log of S&P 500 Dividend by Month (Inflation Adjusted)
5790 MULTPLSP500DIVMONTH_mva050 S&P 500 Dividend by Month (Inflation Adjusted) 50 Day MA
5797 CHRISCMEHG1_Log Log of Copper Futures, Continuous Contract #1 (HG1) (Front Month)
5800 WWDIWLDISAIRGOODMTK1_YoY Air transport, freight Year over Year
5806 WWDIWLDISAIRGOODMTK1_Log Log of Air transport, freight
5807 WWDIWLDISAIRGOODMTK1_mva200 Air transport, freight 200 Day MA
5808 WWDIWLDISAIRGOODMTK1_mva050 Air transport, freight 50 Day MA
5814 BKRTotal_SmoothDer Derivative of Smoothed Total Rig Count
5815 BKRTotal_Log Log of Total Rig Count
5816 BKRTotal_mva200 Total Rig Count 200 Day MA
5817 BKRTotal_mva050 Total Rig Count 50 Day MA
5823 BKRGas_SmoothDer Derivative of Smoothed Gas Rig Count
5824 BKRGas_Log Log of Gas Rig Count
5825 BKRGas_mva200 Gas Rig Count 200 Day MA
5826 BKRGas_mva050 Gas Rig Count 50 Day MA
5833 BKROil_Log Log of Oil Rig Count
5834 BKROil_mva200 Oil Rig Count 200 Day MA
5835 BKROil_mva050 Oil Rig Count 50 Day MA
5836 FARMINCOME_YoY Net Farm Income Year over Year
5842 FARMINCOME_Log Log of Net Farm Income
5843 FARMINCOME_mva200 Net Farm Income 200 Day MA
5844 FARMINCOME_mva050 Net Farm Income 50 Day MA
5848 OPEARNINGSPERSHARE_Smooth Savitsky-Golay Smoothed (p=3, n=365) Operating Earnings per Share
5851 OPEARNINGSPERSHARE_Log Log of Operating Earnings per Share
5852 OPEARNINGSPERSHARE_mva200 Operating Earnings per Share 200 Day MA
5853 OPEARNINGSPERSHARE_mva050 Operating Earnings per Share 50 Day MA
5857 AREARNINGSPERSHARE_Smooth Savitsky-Golay Smoothed (p=3, n=365) As-Reported Earnings per Share
5860 AREARNINGSPERSHARE_Log Log of As-Reported Earnings per Share
5861 AREARNINGSPERSHARE_mva200 As-Reported Earnings per Share 200 Day MA
5862 AREARNINGSPERSHARE_mva050 As-Reported Earnings per Share 50 Day MA
5866 CASHDIVIDENDSPERSHR_Smooth Savitsky-Golay Smoothed (p=3, n=365) Cash Dividends per Share
5868 CASHDIVIDENDSPERSHR_SmoothDer Derivative of Smoothed Cash Dividends per Share
5869 CASHDIVIDENDSPERSHR_Log Log of Cash Dividends per Share
5870 CASHDIVIDENDSPERSHR_mva200 Cash Dividends per Share 200 Day MA
5871 CASHDIVIDENDSPERSHR_mva050 Cash Dividends per Share 50 Day MA
5877 SALESPERSHR_SmoothDer Derivative of Smoothed Sales per Share
5878 SALESPERSHR_Log Log of Sales per Share
5880 SALESPERSHR_mva050 Sales per Share 50 Day MA
5884 BOOKVALPERSHR_Smooth Savitsky-Golay Smoothed (p=3, n=365) Book value per Share
5887 BOOKVALPERSHR_Log Log of Book value per Share
5888 BOOKVALPERSHR_mva200 Book value per Share 200 Day MA
5889 BOOKVALPERSHR_mva050 Book value per Share 50 Day MA
5895 CAPEXPERSHR_SmoothDer Derivative of Smoothed Cap ex per Share
5896 CAPEXPERSHR_Log Log of Cap ex per Share
5898 CAPEXPERSHR_mva050 Cap ex per Share 50 Day MA
5902 PRICE_Smooth Savitsky-Golay Smoothed (p=3, n=365) Price
5905 PRICE_Log Log of Price
5906 PRICE_mva200 Price 200 Day MA
5907 PRICE_mva050 Price 50 Day MA
5911 OPEARNINGSTTM_Smooth Savitsky-Golay Smoothed (p=3, n=365) TTM Operating Earnings
5914 OPEARNINGSTTM_Log Log of TTM Operating Earnings
5915 OPEARNINGSTTM_mva200 TTM Operating Earnings 200 Day MA
5916 OPEARNINGSTTM_mva050 TTM Operating Earnings 50 Day MA
5920 AREARNINGSTTM_Smooth Savitsky-Golay Smoothed (p=3, n=365) TTM Reported Earnings
5923 AREARNINGSTTM_Log Log of TTM Reported Earnings
5924 AREARNINGSTTM_mva200 TTM Reported Earnings 200 Day MA
5925 AREARNINGSTTM_mva050 TTM Reported Earnings 50 Day MA
5933 FINRAMarginDebt_mva200 Margin Debt 200 Day MA
5940 FINRAFreeCreditMargin_SmoothDer Derivative of Smoothed Free Credit Balances in Customers’ Securities Margin Accounts
5944 OCCEquityVolume_YoY Equity Options Volume Year over Year
5950 OCCEquityVolume_Log Log of Equity Options Volume
5951 OCCEquityVolume_mva200 Equity Options Volume 200 Day MA
5952 OCCEquityVolume_mva050 Equity Options Volume 50 Day MA
5953 OCCNonEquityVolume_YoY Non-Equity Options Volume Year over Year
5959 OCCNonEquityVolume_Log Log of Non-Equity Options Volume
5960 OCCNonEquityVolume_mva200 Non-Equity Options Volume 200 Day MA
5961 OCCNonEquityVolume_mva050 Non-Equity Options Volume 50 Day MA
5965 RSALESAGG_Smooth Savitsky-Golay Smoothed (p=3, n=365) Real Retail and Food Services Sales (RRSFS and RSALES)
5977 BUSLOANS.minus.BUSLOANSNSA_Log Log of Business Loans (Montlhy) SA - NSA
5978 BUSLOANS.minus.BUSLOANSNSA_mva200 Business Loans (Montlhy) SA - NSA 200 Day MA
5986 BUSLOANS.minus.BUSLOANSNSA.by.GDP_Log Log of Business Loans (Montlhy) SA - NSA divided by GDP
5987 BUSLOANS.minus.BUSLOANSNSA.by.GDP_mva200 Business Loans (Montlhy) SA - NSA divided by GDP 200 Day MA
5989 BUSLOANS.by.GDP_YoY Business Loans Normalized by GDP Year over Year
5990 BUSLOANS.by.GDP_YoY4 Business Loans Normalized by GDP 4 Year over 4 Year
5991 BUSLOANS.by.GDP_YoY5 Business Loans Normalized by GDP 5 Year over 5 Year
5992 BUSLOANS.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans Normalized by GDP
5994 BUSLOANS.by.GDP_SmoothDer Derivative of Smoothed Business Loans Normalized by GDP
6001 BUSLOANS.INTEREST_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Monthly, SA) Adjusted Interest Burdens
6010 BUSLOANS.INTEREST.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Monthly, SA) Adjusted Interest Burden Divided by GDP
6012 BUSLOANS.INTEREST.by.GDP_SmoothDer Derivative of Smoothed Business Loans (Monthly, SA) Adjusted Interest Burden Divided by GDP
6016 BUSLOANSNSA.by.GDP_YoY Business Loans Normalized by GDP Year over Year
6017 BUSLOANSNSA.by.GDP_YoY4 Business Loans Normalized by GDP 4 Year over 4 Year
6019 BUSLOANSNSA.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans Normalized by GDP
6021 BUSLOANSNSA.by.GDP_SmoothDer Derivative of Smoothed Business Loans Normalized by GDP
6025 TOTCI.by.GDP_YoY Business Loans (Weekly, SA) Normalized by GDP Year over Year
6027 TOTCI.by.GDP_YoY5 Business Loans (Weekly, SA) Normalized by GDP 5 Year over 5 Year
6028 TOTCI.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Weekly, SA) Normalized by GDP
6030 TOTCI.by.GDP_SmoothDer Derivative of Smoothed Business Loans (Weekly, SA) Normalized by GDP
6031 TOTCI.by.GDP_Log Log of Business Loans (Weekly, SA) Normalized by GDP
6034 TOTCINSA.by.GDP_YoY Business Loans (Weekly, NSA) Normalized by GDP Year over Year
6036 TOTCINSA.by.GDP_YoY5 Business Loans (Weekly, NSA) Normalized by GDP 5 Year over 5 Year
6037 TOTCINSA.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Weekly, NSA) Normalized by GDP
6039 TOTCINSA.by.GDP_SmoothDer Derivative of Smoothed Business Loans (Weekly, NSA) Normalized by GDP
6040 TOTCINSA.by.GDP_Log Log of Business Loans (Weekly, NSA) Normalized by GDP
6046 TOTCINSA.INTEREST_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (Weekly, NSA) Adjusted Interest Burdens
6055 TOTCINSA.INTEREST.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Business Loans (weekly, NSA) Adjusted Interest Burden Divided by GDP
6057 TOTCINSA.INTEREST.by.GDP_SmoothDer Derivative of Smoothed Business Loans (weekly, NSA) Adjusted Interest Burden Divided by GDP
6061 W875RX1.by.GDP_YoY Real Personal Income Normalized by GDP Year over Year
6062 W875RX1.by.GDP_YoY4 Real Personal Income Normalized by GDP 4 Year over 4 Year
6066 W875RX1.by.GDP_SmoothDer Derivative of Smoothed Real Personal Income Normalized by GDP
6070 A065RC1A027NBEA.by.GDP_YoY Personal Income (NSA) Normalized by GDP Year over Year
6071 A065RC1A027NBEA.by.GDP_YoY4 Personal Income (NSA) Normalized by GDP 4 Year over 4 Year
6075 A065RC1A027NBEA.by.GDP_SmoothDer Derivative of Smoothed Personal Income (NSA) Normalized by GDP
6076 A065RC1A027NBEA.by.GDP_Log Log of Personal Income (NSA) Normalized by GDP
6082 PI.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Personal Income (SA) Normalized by GDP
6084 PI.by.GDP_SmoothDer Derivative of Smoothed Personal Income (SA) Normalized by GDP
6087 PI.by.GDP_mva050 Personal Income (SA) Normalized by GDP 50 Day MA
6091 A053RC1Q027SBEA.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
6094 A053RC1Q027SBEA.by.GDP_Log Log of National income: Corporate profits before tax (without IVA and CCAdj) Normalized by GDP
6099 CPROFIT.by.GDP_YoY5 National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP 5 Year over 5 Year
6100 CPROFIT.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
6103 CPROFIT.by.GDP_Log Log of National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP
6104 CPROFIT.by.GDP_mva200 National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP 200 Day MA
6105 CPROFIT.by.GDP_mva050 National income: Corporate profits before tax (with IVA and CCAdj) Normalized by GDP 50 Day MA
6106 CONSUMERNSA.by.GDP_YoY Consumer Loans Not Seasonally Adjusted divided by GDP Year over Year
6111 CONSUMERNSA.by.GDP_SmoothDer Derivative of Smoothed Consumer Loans Not Seasonally Adjusted divided by GDP
6112 CONSUMERNSA.by.GDP_Log Log of Consumer Loans Not Seasonally Adjusted divided by GDP
6113 CONSUMERNSA.by.GDP_mva200 Consumer Loans Not Seasonally Adjusted divided by GDP 200 Day MA
6114 CONSUMERNSA.by.GDP_mva050 Consumer Loans Not Seasonally Adjusted divided by GDP 50 Day MA
6115 RREACBM027NBOG.by.GDP_YoY Residental Real Estate Loans (Monthly, NSA) divided by GDP Year over Year
6116 RREACBM027NBOG.by.GDP_YoY4 Residental Real Estate Loans (Monthly, NSA) divided by GDP 4 Year over 4 Year
6117 RREACBM027NBOG.by.GDP_YoY5 Residental Real Estate Loans (Monthly, NSA) divided by GDP 5 Year over 5 Year
6120 RREACBM027NBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Monthly, NSA) divided by GDP
6121 RREACBM027NBOG.by.GDP_Log Log of Residental Real Estate Loans (Monthly, NSA) divided by GDP
6123 RREACBM027NBOG.by.GDP_mva050 Residental Real Estate Loans (Monthly, NSA) divided by GDP 50 Day MA
6124 RREACBM027SBOG.by.GDP_YoY Residental Real Estate Loans (Monthly, SA) divided by GDP Year over Year
6125 RREACBM027SBOG.by.GDP_YoY4 Residental Real Estate Loans (Monthly, SA) divided by GDP 4 Year over 4 Year
6126 RREACBM027SBOG.by.GDP_YoY5 Residental Real Estate Loans (Monthly, SA) divided by GDP 5 Year over 5 Year
6127 RREACBM027SBOG.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Monthly, SA) divided by GDP
6129 RREACBM027SBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Monthly, SA) divided by GDP
6130 RREACBM027SBOG.by.GDP_Log Log of Residental Real Estate Loans (Monthly, SA) divided by GDP
6132 RREACBM027SBOG.by.GDP_mva050 Residental Real Estate Loans (Monthly, SA) divided by GDP 50 Day MA
6133 RREACBW027SBOG.by.GDP_YoY Residental Real Estate Loans (Weekly, SA) divided by GDP Year over Year
6134 RREACBW027SBOG.by.GDP_YoY4 Residental Real Estate Loans (Weekly, SA) divided by GDP 4 Year over 4 Year
6135 RREACBW027SBOG.by.GDP_YoY5 Residental Real Estate Loans (Weekly, SA) divided by GDP 5 Year over 5 Year
6136 RREACBW027SBOG.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, SA) divided by GDP
6138 RREACBW027SBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Weekly, SA) divided by GDP
6139 RREACBW027SBOG.by.GDP_Log Log of Residental Real Estate Loans (Weekly, SA) divided by GDP
6141 RREACBW027SBOG.by.GDP_mva050 Residental Real Estate Loans (Weekly, SA) divided by GDP 50 Day MA
6142 RREACBW027NBOG.by.GDP_YoY Residental Real Estate Loans (Weekly, NSA) divided by GDP Year over Year
6145 RREACBW027NBOG.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Residental Real Estate Loans (Weekly, NSA) divided by GDP
6147 RREACBW027NBOG.by.GDP_SmoothDer Derivative of Smoothed Residental Real Estate Loans (Weekly, NSA) divided by GDP
6150 RREACBW027NBOG.by.GDP_mva050 Residental Real Estate Loans (Weekly, NSA) divided by GDP 50 Day MA
6152 UMDMNO.by.GDP_YoY4 Durable Goods (Monthly, NSA) divided by GDP 4 Year over 4 Year
6153 UMDMNO.by.GDP_YoY5 Durable Goods (Monthly, NSA) divided by GDP 5 Year over 5 Year
6161 DGORDER.by.GDP_YoY4 Durable Goods (Monthly, NSA) divided by GDP 4 Year over 4 Year
6163 DGORDER.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Durable Goods (Monthly, NSA) divided by GDP
6165 DGORDER.by.GDP_SmoothDer Derivative of Smoothed Durable Goods (Monthly, NSA) divided by GDP
6166 DGORDER.by.GDP_Log Log of Durable Goods (Monthly, NSA) divided by GDP
6167 DGORDER.by.GDP_mva200 Durable Goods (Monthly, NSA) divided by GDP 200 Day MA
6168 DGORDER.by.GDP_mva050 Durable Goods (Monthly, NSA) divided by GDP 50 Day MA
6169 ASHMA.by.GDP_YoY Home Mortgages (Quarterly, NSA) divided by GDP Year over Year
6170 ASHMA.by.GDP_YoY4 Home Mortgages (Quarterly, NSA) divided by GDP 4 Year over 4 Year
6171 ASHMA.by.GDP_YoY5 Home Mortgages (Quarterly, NSA) divided by GDP 5 Year over 5 Year
6174 ASHMA.by.GDP_SmoothDer Derivative of Smoothed Home Mortgages (Quarterly, NSA) divided by GDP
6175 ASHMA.by.GDP_Log Log of Home Mortgages (Quarterly, NSA) divided by GDP
6177 ASHMA.by.GDP_mva050 Home Mortgages (Quarterly, NSA) divided by GDP 50 Day MA
6181 ASHMA.INTEREST_Smooth Savitsky-Golay Smoothed (p=3, n=365) Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens
6185 ASHMA.INTEREST_mva200 Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 200 Day MA
6186 ASHMA.INTEREST_mva050 Home Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 50 Day MA
6190 ASHMA.INTEREST.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365)
6194 ASHMA.INTEREST.by.GDP_mva200 200 Day MA
6195 ASHMA.INTEREST.by.GDP_mva050 50 Day MA
6201 CONSUMERNSA.INTEREST_SmoothDer Derivative of Smoothed Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6202 CONSUMERNSA.INTEREST_Log Log of Consumer Loans (Not Seasonally Adjusted) Interest Burdens
6203 CONSUMERNSA.INTEREST_mva200 Consumer Loans (Not Seasonally Adjusted) Interest Burdens 200 Day MA
6204 CONSUMERNSA.INTEREST_mva050 Consumer Loans (Not Seasonally Adjusted) Interest Burdens 50 Day MA
6205 CONSUMERNSA.INTEREST.by.GDP_YoY Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP Year over Year
6208 CONSUMERNSA.INTEREST.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6210 CONSUMERNSA.INTEREST.by.GDP_SmoothDer Derivative of Smoothed Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6211 CONSUMERNSA.INTEREST.by.GDP_Log Log of Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP
6212 CONSUMERNSA.INTEREST.by.GDP_mva200 Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 200 Day MA
6213 CONSUMERNSA.INTEREST.by.GDP_mva050 Consumer Loans (Not Seasonally Adjusted) Interest Burden Divided by GDP 50 Day MA
6214 TOTLNNSA_YoY Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA) Year over Year
6217 TOTLNNSA_Smooth Savitsky-Golay Smoothed (p=3, n=365) Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
6219 TOTLNNSA_SmoothDer Derivative of Smoothed Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
6220 TOTLNNSA_Log Log of Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA)
6221 TOTLNNSA_mva200 Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA) 200 Day MA
6222 TOTLNNSA_mva050 Total Loans Not Seasonally Adjusted (BUSLOANS+REALLNSA+CONSUMERNSA) 50 Day MA
6223 TOTLNNSA.by.GDP_YoY Total Loans Not Seasonally Adjusted divided by GDP Year over Year
6224 TOTLNNSA.by.GDP_YoY4 Total Loans Not Seasonally Adjusted divided by GDP 4 Year over 4 Year
6225 TOTLNNSA.by.GDP_YoY5 Total Loans Not Seasonally Adjusted divided by GDP 5 Year over 5 Year
6226 TOTLNNSA.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Total Loans Not Seasonally Adjusted divided by GDP
6228 TOTLNNSA.by.GDP_SmoothDer Derivative of Smoothed Total Loans Not Seasonally Adjusted divided by GDP
6229 TOTLNNSA.by.GDP_Log Log of Total Loans Not Seasonally Adjusted divided by GDP
6231 TOTLNNSA.by.GDP_mva050 Total Loans Not Seasonally Adjusted divided by GDP 50 Day MA
6235 TOTLNNSA.INTEREST_Smooth Savitsky-Golay Smoothed (p=3, n=365) Total Loans Not Seasonally Adjusted Interest Burdens
6244 TOTLNNSA.INTEREST.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Total Loans Not Seasonally Adjusted Interest Burden Divided by GDP
6251 WRESBAL.by.GDP_YoY4 Reserve Balances with Federal Reserve Banks Divided by GDP 4 Year over 4 Year
6252 WRESBAL.by.GDP_YoY5 Reserve Balances with Federal Reserve Banks Divided by GDP 5 Year over 5 Year
6253 WRESBAL.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Reserve Balances with Federal Reserve Banks Divided by GDP
6257 WRESBAL.by.GDP_mva200 Reserve Balances with Federal Reserve Banks Divided by GDP 200 Day MA
6260 EXCSRESNW.by.GDP_YoY4 Excess Reserves of Depository Institutions Divided by GDP 4 Year over 4 Year
6261 EXCSRESNW.by.GDP_YoY5 Excess Reserves of Depository Institutions Divided by GDP 5 Year over 5 Year
6264 EXCSRESNW.by.GDP_SmoothDer Derivative of Smoothed Excess Reserves of Depository Institutions Divided by GDP
6265 EXCSRESNW.by.GDP_Log Log of Excess Reserves of Depository Institutions Divided by GDP
6275 WLRRAL.by.GDP_mva200 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 200 Day MA
6276 WLRRAL.by.GDP_mva050 Liabilities and Capital: Liabilities: Reverse Repurchase Agreements: Wednesday Level (NSA) Divided by GDP 50 Day MA
6280 SOFR99.minus.SOFR1_Smooth Savitsky-Golay Smoothed (p=3, n=365) Secured Overnight Financing Rate: 99th Percentile - 1st Percentile
6289 EXPCH.minus.IMPCH_Smooth Savitsky-Golay Smoothed (p=3, n=365) U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis)
6291 EXPCH.minus.IMPCH_SmoothDer Derivative of Smoothed U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis)
6292 EXPCH.minus.IMPCH_Log Log of U.S. Exports to China (FAS Basis) - U.S. Imports to China (Customs Basis)
6295 EXPMX.minus.IMPMX_YoY Year over Year
6296 EXPMX.minus.IMPMX_YoY4 4 Year over 4 Year
6297 EXPMX.minus.IMPMX_YoY5 5 Year over 5 Year
6301 EXPMX.minus.IMPMX_Log Log of
6304 SRPSABSNNCB.by.GDP_YoY Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP Year over Year
6305 SRPSABSNNCB.by.GDP_YoY4 Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 4 Year over 4 Year
6306 SRPSABSNNCB.by.GDP_YoY5 Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP 5 Year over 5 Year
6307 SRPSABSNNCB.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
6310 SRPSABSNNCB.by.GDP_Log Log of Nonfinancial corporate business; security repurchase agreements; asset, Level (NSA) Divided by GDP
6313 ASTLL.by.GDP_YoY All sectors; total loans; liability, Level (NSA) Divided by GDP Year over Year
6314 ASTLL.by.GDP_YoY4 All sectors; total loans; liability, Level (NSA) Divided by GDP 4 Year over 4 Year
6318 ASTLL.by.GDP_SmoothDer Derivative of Smoothed All sectors; total loans; liability, Level (NSA) Divided by GDP
6319 ASTLL.by.GDP_Log Log of All sectors; total loans; liability, Level (NSA) Divided by GDP
6322 ASFMA.by.GDP_YoY All sectors; farm mortgages; asset, Level (NSA) Divided by GDP Year over Year
6323 ASFMA.by.GDP_YoY4 All sectors; farm mortgages; asset, Level (NSA) Divided by GDP 4 Year over 4 Year
6327 ASFMA.by.GDP_SmoothDer Derivative of Smoothed All sectors; farm mortgages; asset, Level (NSA) Divided by GDP
6328 ASFMA.by.GDP_Log Log of All sectors; farm mortgages; asset, Level (NSA) Divided by GDP
6336 ASFMA.by.ASTLL_SmoothDer Derivative of Smoothed All sectors; total loans Divided by farm mortgages
6337 ASFMA.by.ASTLL_Log Log of All sectors; total loans Divided by farm mortgages
6343 ASFMA.INTEREST_Smooth Savitsky-Golay Smoothed (p=3, n=365) Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens
6347 ASFMA.INTEREST_mva200 Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 200 Day MA
6348 ASFMA.INTEREST_mva050 Farm Mortgages (Quarterly, NSA) 30-Year Fixed Interest Burdens 50 Day MA
6352 ASFMA.INTEREST.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP
6354 ASFMA.INTEREST.by.GDP_SmoothDer Derivative of Smoothed Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP
6357 ASFMA.INTEREST.by.GDP_mva050 Farm Mortgages (Quarterly, NSA) Interest Burden Divided by GDP 50 Day MA
6358 FARMINCOME.by.GDP_YoY Farm Income (Annual, NSA) Divided by GDP Year over Year
6363 FARMINCOME.by.GDP_SmoothDer Derivative of Smoothed Farm Income (Annual, NSA) Divided by GDP
6364 FARMINCOME.by.GDP_Log Log of Farm Income (Annual, NSA) Divided by GDP
6368 BOGMBASE.by.GDP_YoY4 BOGMBASE Divided by GDP 4 Year over 4 Year
6370 BOGMBASE.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) BOGMBASE Divided by GDP
6373 BOGMBASE.by.GDP_Log Log of BOGMBASE Divided by GDP
6374 BOGMBASE.by.GDP_mva200 BOGMBASE Divided by GDP 200 Day MA
6375 BOGMBASE.by.GDP_mva050 BOGMBASE Divided by GDP 50 Day MA
6379 WALCL.by.GDP_Smooth Savitsky-Golay Smoothed (p=3, n=365) All Federal Reserve Banks: Total Assets Divided by GDP
6381 WALCL.by.GDP_SmoothDer Derivative of Smoothed All Federal Reserve Banks: Total Assets Divided by GDP
6383 WALCL.by.GDP_mva200 All Federal Reserve Banks: Total Assets Divided by GDP 200 Day MA
6384 WALCL.by.GDP_mva050 All Federal Reserve Banks: Total Assets Divided by GDP 50 Day MA
6385 ECBASSETS.by.EUNNGDP_YoY Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP Year over Year
6390 ECBASSETS.by.EUNNGDP_SmoothDer Derivative of Smoothed Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP
6391 ECBASSETS.by.EUNNGDP_Log Log of Central Bank Assets for Euro Area (11-19 Countries) Divided by GDP
6400 DGS30TO10_Log Log of Yield Curve, 30 and 10 Year Treasury (DGS30-DGS10)
6409 DGS10TO1_Log Log of Yield Curve, 10 and 1 Year Treasury (DGS10-DGS1)
6418 DGS10TO2_Log Log of Yield Curve, 10 and 2 Year Treasury (DGS10-DGS2)
6424 DGS10TOTB3MS_Smooth Savitsky-Golay Smoothed (p=3, n=365) Yield Curve, 10 and 3 Month Treasury (DGS10-TB3MS)
6427 DGS10TOTB3MS_Log Log of Yield Curve, 10 and 3 Month Treasury (DGS10-TB3MS)
6433 DGS10TODTB3_Smooth Savitsky-Golay Smoothed (p=3, n=365) Yield Curve, 10 and 3 Month Treasury (DGS10-DTB3)
6436 DGS10TODTB3_Log Log of Yield Curve, 10 and 3 Month Treasury (DGS10-DTB3)
6457 UNEMPLOYBYPOPTHM_YoY Unemployment level, seasonally adjusted / Population Year over Year
6469 NPPTTLBYPOPTHM_Smooth Savitsky-Golay Smoothed (p=3, n=365) ADP Private Employment / Population
6472 NPPTTLBYPOPTHM_Log Log of ADP Private Employment / Population
6473 NPPTTLBYPOPTHM_mva200 ADP Private Employment / Population 200 Day MA
6474 NPPTTLBYPOPTHM_mva050 ADP Private Employment / Population 50 Day MA
6475 U6toU3_YoY U6RATE minums UNRATE Year over Year
6477 U6toU3_YoY5 U6RATE minums UNRATE 5 Year over 5 Year
6480 U6toU3_SmoothDer Derivative of Smoothed U6RATE minums UNRATE
6517 DCOILWTICO.by.PPIACO_Log Log of Crude Oil - WTI, $/bbl, Normalized by producer price index c.o.
6525 GOLDAMGBD228NLBM.by.PPIACO_SmoothDer Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by commodities producer price index
6543 GOLDAMGBD228NLBM.by.GDP_SmoothDer Derivative of Smoothed Gold, USD/Troy OUnce, Normalized by GDP
6550 GSG.Close.by.GDPDEF_Smooth Savitsky-Golay Smoothed (p=3, n=365) GSCI Commodity-Indexed Trust, Normalized by GDP def
6554 GSG.Close.by.GDPDEF_mva200 GSCI Commodity-Indexed Trust, Normalized by GDP def 200 Day MA
6572 GDPBYPOPTHM_mva200 GDP/Population 200 Day MA
6599 GSPC.CloseBYMDY.Close_mva200 GSPC by MDY 200 Day MA
6600 GSPC.CloseBYMDY.Close_mva050 GSPC by MDY 50 Day MA
6608 QQQ.CloseBYMDY.Close_mva200 QQQ by MDY 200 Day MA
6609 QQQ.CloseBYMDY.Close_mva050 QQQ by MDY 50 Day MA
6613 GSPC.DailySwing_Smooth Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Daily Swing: (High - Low) / Open
6615 GSPC.DailySwing_SmoothDer Derivative of Smoothed S&P 500 (^GSPC) Daily Swing: (High - Low) / Open
6616 GSPC.DailySwing_Log Log of S&P 500 (^GSPC) Daily Swing: (High - Low) / Open
6622 GSPC.Open.by.GDPDEF_Smooth Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Open divided by GDP deflator
6626 GSPC.Open.by.GDPDEF_mva200 S&P 500 (^GSPC) Open divided by GDP deflator 200 Day MA
6627 GSPC.Open.by.GDPDEF_mva050 S&P 500 (^GSPC) Open divided by GDP deflator 50 Day MA
6631 GSPC.Close.by.GDPDEF_Smooth Savitsky-Golay Smoothed (p=3, n=365) S&P 500 (^GSPC) Close divided by GDP deflator
6635 GSPC.Close.by.GDPDEF_mva200 S&P 500 (^GSPC) Close divided by GDP deflator 200 Day MA
6636 GSPC.Close.by.GDPDEF_mva050 S&P 500 (^GSPC) Close divided by GDP deflator 50 Day MA
6643 HNFSUSNSA.minus.HSN1FNSA_Log Log of Houses for sale - houses sold
6644 HNFSUSNSA.minus.HSN1FNSA_mva200 Houses for sale - houses sold 200 Day MA
6645 HNFSUSNSA.minus.HSN1FNSA_mva050 Houses for sale - houses sold 50 Day MA
6649 MSPUS.times.HOUST_Smooth Savitsky-Golay Smoothed (p=3, n=365) New privately owned units start times median price
6651 MSPUS.times.HOUST_SmoothDer Derivative of Smoothed New privately owned units start times median price
6652 MSPUS.times.HOUST_Log Log of New privately owned units start times median price
6653 MSPUS.times.HOUST_mva200 New privately owned units start times median price 200 Day MA
6654 MSPUS.times.HOUST_mva050 New privately owned units start times median price 50 Day MA
6656 MSPUS.times.HNFSUSNSA_YoY4 New privately owned 1-family units for sale times median price 4 Year over 4 Year
6661 MSPUS.times.HNFSUSNSA_Log Log of New privately owned 1-family units for sale times median price
6662 MSPUS.times.HNFSUSNSA_mva200 New privately owned 1-family units for sale times median price 200 Day MA
6663 MSPUS.times.HNFSUSNSA_mva050 New privately owned 1-family units for sale times median price 50 Day MA
6671 GSPC.Open_mva050_mva200_sig Sell Signal S&P 500 50 SMA - 200 SMA
6672 MULTPLSP500PERATIOMONTH_Mean S&P 500 TTM P/E Average (Excludes Values Greater Than 50)

Equities

Equity indexes normalized by GDP

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

The last two years compare favorably with the period around the late 1950’s. Need to dig into this one.

datay <- "GSPC.Close"
ylim <- c(2000, d.GSPC.max)
my.data <- plotSimilarPeriods(df.data, dfRecession, df.symbols, datay, ylim, i.window = 60)
my.data[[1]]

Look at how the different segments of the market move

datay <- "GSPC.CloseBYMDY.Close_YoY"
ylim <- c(-50, 75)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

datay <- "GSPC.CloseBYMDY.Close"
ylim <- c(0, 20)
dtStart = as.Date('1980-01-01')
plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)

S&P 500 Normalized moving average

Look at moving average relationship by dividing the S&P 500 open price by the 200 day SMA.

datay <- "GSPC.Open_mva200_Norm"
ylim <- c(50, 125)
dt.start = as.Date('2008-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Crossovers

Look at the 50 DMA versus 200 DMA, often used as a technical indicator of market direction.

datay <- "GSPC.Open_mva050_mva200"
ylim <- c(-200, 200)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

datay <- "GSPC.Open_mva050_mva200_sig "
ylim <- c(0.0, 1.0)
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStartBackTest)

S&P 500 TTM P/E

Take a look at some of the earnings trends from SilverBlatt’s sheet.

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Take a longer look back at as-reported and operating earnings

Market prices can out-run earnings so take a look at price to earnings.

Focus on some of the more recent activity

S&P 500 Sales

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 1500)
dt.start <- as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

datay <- "MULTPLSP500SALESQUARTER"
ylim <- c(500, 1500)
dt.start = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Unit Profits

The series peaks in the middle of a bull market.

S&P 500 dividends

12-month real dividend per share inflation adjusted November, 2018 dollars. Data courtesy Standard & Poor’s and Robert Shiller.

https://www.quandl.com/data/MULTPL/SP500_DIV_MONTH-S-P-500-Dividend-by-Month

Evaluate year over year dividend growth.

Real value dividend growth.

datay <- "MULTPLSP500DIVMONTH_YoY"
ylim <- c(-40, 20)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 dividend yield (12 month dividend per share)/price. Yields following September 2018 (including the current yield) are estimated based on 12 month dividends through September 2018, as reported by S&P. Sources: Standard & Poor’s for current S&P 500 Dividend Yield. Robert Shiller and his book Irrational Exuberance for historic S&P 500 Dividend Yields.

https://www.quandl.com/data/MULTPL/SP500_DIV_YIELD_MONTH-S-P-500-Dividend-Yield-by-Month

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(0, 12)
dtStart = as.Date('1950-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

datay <- "MULTPLSP500DIVYIELDMONTH"
ylim <- c(1, 4)
dtStart = as.Date('2001-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart, b.percentile = FALSE)

S&P 500 Volume

The log of the S&P volume has some interesting patterns, but nothing that seems to help with a recession indicator.

That is one spiky data series. Not sure there is a lot to help us here.

Russell 2000

Take a look at recent activity in the small cap market.

S&P 500 to Rusell 2000

Thirty day movement

Correlation

## Warning in max.default(structure(numeric(0), class = "Date"),
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## Warning in min.default(structure(numeric(0), class = "Date"),
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S&P 500 to MDY (Mid-cap) 2000 Correlation

datay1 <- "RLG.Open"
ylim1 <- c(0, 2500)

datay2 <- "MDY.Open"
ylim2 <- c(0, 500)

dtStart <- as.Date("1jan2003","%d%b%Y")

w <- 30
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)
## Warning in max.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to max;
## returning -Inf
## Warning in min.default(structure(numeric(0), class = "Date"),
## structure(numeric(0), class = "Date"), : no non-missing arguments to min;
## returning Inf

Dividend Stocks

This is an interesting series, they should perform better through the recessions. Unfortunately they are short lived so there is not much data so this is more of a place holder for now.

datay <- "NOBL.Open"
ylim <- c(40, 110)
dt.start <- as.Date('2014-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dt.start)

Margin and option data

NYSE Margin Debt

Taking a look at margin debt. NYXDATA stopped providing NYSE margin debt data on Dec 2017. Data is available from FINRA, but it includes more accounts than the data did for NYXdata. I stitched togeter the data sets: data after Jan 2010 include NYSE+Others, data prior is just NYSE account data scaled up to match the FINRA data.

It tends to creep up when there is a frenzy in the stock market.

datay <- "FINRAMarginDebt_Log"
ylim <- c(5, 15)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Take a close look at recent activity

Sometimes it is more helpful to view year over year growth.

More near-term trend.

Take a look at some of the correlations

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

Comparison to the Russell 2000

datay1 <- "FINRAMarginDebt_YoY"
ylim1 <- c(-100, 100)

datay2 <- "RLG.Close_YoY"
ylim2 <- c(-100, 100)

dtStart <- as.Date("1jan1995","%d%b%Y")

w <- 90
corrName <-
  calcRollingCorr(dfRecession,
                  df.data,
                  df.symbols,
                  datay1,
                  ylim1,
                  datay2,
                  ylim2,
                  w,
                  dtStart)

OCC Options Volumes

See what is happening with the options volumes for equities. (From: https://www.theocc.com/webapps/historical-volume-query)

Looks like options on non-equity co-occurs with peaks/troughs?.

Market Volatility

Take a look at some of the indications of market volatility

CBOE VIX

As markets become complacent (low VIX) and high values, peaks often occur.

Compare the VIX to some of the ETF’s out there.

There

Not much predictive in VIX, take a quick look at the smoothed derivative.

S&P Daily Swings

Daily changes in the S&P should correlate well with the VIX.

More of a correlating series than a predictor.

Employment and payrolls

Unemployment rates

Unemployment rates will probably be useful, let’s take a look at the U-3. The data is a little noisy so there is also a smoothed version plotted. There seems to be a relationship between the unemployment rate and the recessions, but it could be a lagging indicator. This will be explored a little bit more later.

Looking at the unemployment rate, the eye is drawn to the rise and fall of the data, this suggests that the derivative might be helpful as well. The figure below shows the results, using a Savitzky-Golay FIR filter. It looks like the unemployment rate peaks in the middel of the recession. That peak might be a good buy signal.

Continuing Claims

A good measure of how much unemployment is growing.

Continued claims, also referred to as insured unemployment, is the number of people who have already filed an initial claim and who have experienced a week of unemployment and then filed a continued claim to claim benefits for that week of unemployment. Continued claims data are based on the week of unemployment, not the week when the initial claim was filed

https://fred.stlouisfed.org/series/CCNSA

A good measure of how much unemployment is growing

Initial Claims

A good measure of how much unemployment is growing.

An initial claim is a claim filed by an unemployed individual after a separation from an employer. The claim requests a determination of basic eligibility for the Unemployment Insurance program.

https://fred.stlouisfed.org/series/ICSA

Unemployment rates, year-over-year

Both the headline unemployment and U-6 number changes are similar. During the upswing on the cycle it does look like the headline number falls faster than U-6

The second derivative of the unemployment rate does have zero crossings near the middle point of a recession. This would make it a helpful buy signal for the trading strategy.

Unemployment rates, similar periods

Historically the last two years of record low unemployment appear most similar to the 1971-1973 time frame. Just before inflation took off.

Unemployment rates, U-6 and headline number.

Let’s also take a look at the total unemployed, U-6. It continues to fall as the headline number stabilizes as people return to the work force. An indicator the cycle is beginning to top out.

Difference between U6 and U3 to see how close the economy is getting to full employment.

Unemployment and market bottoms

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Initial jobless claims

We will also take a look at initial jobless claims, this should start to rise just before the unemployment rate.

It looks like the jobless claim tend to peak more towards the end of the recession. It does not seem to be as strong of a sell indicator as the U-3 rate.

Jobless claims have a seasonal component to them. One way to reduce this effect is to calculate year over year growth. That helps some, the peaks seem to be more closely aligned with the middle to end of recessions.

Take a closer look at recent data

Take a look at the percentage of the population looking for work

A bit more recent trend

Unemployment Level

ADP data here. comes out before the official numbers.

Look at the year-over-year change in ADP.

ADP data divided by the population

Payrolls

Look at the BLS data on payrolls. Check the NSA series, then we will look at YoY data.

Hours worked

Sparked by an article at Mises (https://mises.org/wire/how-alexandria-ocasio-cortez-misunderstands-american-poverty), take a look at average weekly hours

The time series is pretty lumpy, plot the YoY change

A more recent look at average weekly hours of production

Industrial Production

Industrial production is also known to fall during an economic downturm, let’s take a look at some of the data from the FRED on industrual production. It does seem to peak prior to a recession so let’s smooth and look at the derivative as it might be a good indicator as well.

Industrial production over the last ten years or so

The derivative isn’t bad, but it sometimes crosses zeros well into a recession. That is less helpful as either a buy or sell indicator. A better measure might year over year (YoY) change.

The year over year change has a similar appearance. The low values at the beginning make the year over year values larger than the more recent values. Seems like it will rank low a reliable indicator.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 12)

datay2 <- "GSPC.Close_YoY"
ylim2 <- c(-100, 50)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail Sales

Retail sales, aggregate

Retail sales also change during recession. As the plot below shows, it seems to follow the trend of industrial production. It might be too strongly correlated to add much to the model. The will be examined in the correlation section.

The derivative of retail sales is a little more erratic than is was the industrial products. Looks like it might be helpful to include in the model as well.

Retail sales, aggregate year-over-year

Take a look at year-over-year changes

Retail sales and unemployment correlations

Let’s see how that looks on year over year basis. Interesting to compare to unemployment rates there appears to a correlation over the long term.

## Scale for 'y' is already present. Adding another scale for 'y', which will
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There is some similarity. The rolling correlation shows the inverse relationship prior to a recession.

datay1 <- "RSALESAGG_YoY"
ylim1 <- c(-12.5, 7.5)

datay2 <- "UNEMPLOY_YoY"
ylim2 <- c(-30, 100)

dtStart <- as.Date("1jan1970","%d%b%Y")

w <- 180
corrName <- calcRollingCorr(dfRecession,df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Retail sales correlation and industrial production

Industrial production and retail sales look very similar so the plot below shows the 360 correlation. The corerlation does tend to fall around a recession, although 2008 was so bad that they both fell together. Not sure if it is that useful.

datay1 <- "INDPRO"
ylim1 <- c(40, 125)

datay2 <- "RSALESAGG"
ylim2 <- c(100000, 200000)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

It is interesting to see the strong correlation; however, I suspect this is due to more to the shape of the trends. How do the YoY correlations look? They are a little less correlated, probably better to use in the machine learning later.

datay1 <- "INDPRO_YoY"
ylim1 <- c(-20, 20)

datay2 <- "RSALESAGG_YoY"
ylim2 <- c(-20, 20)

dtStart <- as.Date("1jan1981","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Advance Retail Sales

This is an advanced estimate of the retail sales value.

Also take a look at year over year

Retail sales and the labor market

Income

Real Personal Income

Real Personal Income (Excluding Transfer, Annual)

During a recession real personal income falls. In the plot the peaks can be seen prior to each recession.

datay <- "W875RX1"
ylim <- c(3000, 15000)
plotSingleQuickModern(datay, ylim)

The features we are interested in are the peaks and valleys so we’ll use the derivative to get to those. Interesting, there is usually a first zero crossing before a recession and a second during or just after the recession.

Real personal income might have some seasonal variance, but it seems the year over year change tells the same story.

Price and cost measures

This section shows price and cost measures.

Two commonly used indexes are the CPI (consumer price index) and PPI (producer price index). CPI tries to show final prices paid for goods and services by urban U.S. consumers. This index includes sales tax and imports. The PPI attempts to reflect the prices paid at all stages of production, including goods and services purchases as inputs as well as goods and services purchased by consumers from retail and producer sellers. The PPI does not include imports or sales tax. The CPI reflects all rebates and financing plans wherease the PPI reflects only those rebate and financing plans provided by the producer. For example if an automotive manufacturer offers a rebate of $500 and the dealer offers an additional rebate of $500 then the PPI would reflect only the automotive manufacturer rebate, but the CPI would reflect both rebates.

Sources; https://www.bls.gov/opub/hom/pdf/cpihom.pdf and https://www.bls.gov/opub/hom/pdf/ppi-20111028.pdf.

Consumer price index

What does CPI look like?

datay <- "CPIAUCSL"
ylim <- c(0, 300)
plotSingleQuickModern(datay, ylim)

Check out the YoY growth

datay <- "CPIAUCSL_YoY"
ylim <- c(-2, 15)
plotSingleQuickModern(datay, ylim)

CPI to PPI

Suggested by Charlie, it can be helpful to look at the relationship between producer prices and consumer prices.

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Producer Price Index (Commodities)

Commodities

Basket

Take a look at some trends of baskets of commodities.

This plot examines commodity performance relative to the GDP deflator

Crude oil

Look at a trend of West Texas Intermediate (WTI)

This is ticker data from yahoo

Take a look at both WTI and Brent crude.

Real price of crude using producer price index for commodities

Gold

As risks increase investors often flock to safe haven assets like gold. An up-tick in prices can indicate investor uncertainty. This can be seen in the nominal price plot around 1980 and again in 2007.

This plots out the real price of gold by two different deflators. PPI corrected price is a little higher, to be expected since CPI also includes the effects of sales tax and imports. The spike in 1980 is especially pronounced in this series.

See how nominal and real prices look year over year. From the long-term view seems like there is little difference in the three series. Although not shown, even over the near-term there is little difference in the series.

See how gold correlates with the VIX. Both gold and VIX should respond to investor axiety, but it doesn’t look like it correlates very well.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 245 rows containing non-finite values (stat_smooth).

Copper

Dr. Copper has a reputation as an indicator of economic malaise, but it does not seem to have much of a correlation with the recessions. The series below is from CME via Quandl. It has a lot of data so I am also looking at the smoothed version.

Copper is one of the commodities in the PPI so it is a bit of a proxy for how copper is doing relative to the basket of commodities.

The change in prices, year over year, do generally peak prior to a recession. The time and shape of this peak varies, but it still might be helpful. A couple of the large troughs do seem to correlate with the end of the recession. Likely this is because industrial production has also fallen.

There is some correlation between copper and the smooth recession initiator, especially at the end of the recession.

Might be easier to see correlation in a dot plot format.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 342 rows containing non-finite values (stat_smooth).

This is a legacy series from FRED. It has not been updated in a couple of years so I am assuming it will go away.

Oil Services

Amazing events in the first half of 2020, take a look at those

See how the players are doing

Federal Reserve

The federal reserve has an impact on the economy, here are some data series relating to that.

Little bit closer

datay <- "WALCL"
ylim <- c(0, 8500)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Federal Reserve Reverse Repo Agreements

Compare liabilities to reverse repo trends

Spiky, might be easier to look at year-over-year

Normalized by GDP

datay <- "WLRRAL.by.GDP"
ylim <- c(0, 4)
dtStart = as.Date('2003-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Overnight Bank Funding Rate

“The overnight bank funding rate is calculated using federal funds transactions and certain Eurodollar transactions. The federal funds market consists of domestic unsecured borrowings in U.S. dollars by depository institutions from other depository institutions and certain other entities, primarily government-sponsored enterprises, while the Eurodollar market consists of unsecured U.S. dollar deposits held at banks or bank branches outside of the United States. U.S.-based banks can also take Eurodollar deposits domestically through international banking facilities (IBFs). The overnight bank funding rate (OBFR) is calculated as a volume-weighted median of overnight federal funds transactions and Eurodollar transactions reported in the FR 2420 Report of Selected Money Market Rates. Volume-weighted median is the rate associated with transactions at the 50th percentile of transaction volume. Specifically, the volume-weighted median rate is calculated by ordering the transactions from lowest to highest rate, taking the cumulative sum of volumes of these transactions, and identifying the rate associated with the trades at the 50th percentile of dollar volume. The published rates are the volume-weighted median transacted rate, rounded to the nearest basis point.” https://www.newyorkfed.org/markets/obfrinfo.

Secured Overnight Financing Rate

“The Secured Overnight Financing Rate (SOFR) is a broad measure of the cost of borrowing cash overnight collateralized by Treasury securities. The SOFR includes all trades in the Broad General Collateral Rate plus bilateral Treasury repurchase agreement (repo) transactions cleared through the Delivery-versus-Payment (DVP) service offered by the Fixed Income Clearing Corporation (FICC), which is filtered to remove a portion of transactions considered “specials” " https://apps.newyorkfed.org/markets/autorates/sofr

Take a look at the variation (99th - 1st percentile)

Reserve Balances with Federal Reserve Banks

Hard to get a sense of these series in the absolute. Take a look relative to GDP.

By double entry book-keeping reserves+loans (assets) = deposit (liabilities). Does that really work?

Correlation Between Reserves and Total Loans

As reserves increase there should be less lending. That correlation generally holds.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

Did the reserve balances increase after the 2016 and 2018 drops? Not in the same way. There are some relationships between the equities market and the reserves though.

Explicitly correlate reserve balances and total loans. It is a weak and noisy correlation.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 990 rows containing non-finite values (stat_smooth).

Interest on excess reserves

Monetary Base

Currency trend, base

This used to trend along with GDP. It doesn’t anymore.

Money supplies

Basic currency trend (currency component of M1)

datay <- "WCURRNS_YoY"
dtStart = as.Date('1980-01-01')
ylim <- c(0, 17)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

datay <- "WCURRNS_YoY"
dtStart = as.Date('2000-01-01')
ylim <- c(0, 20)
myplot <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)
myplot

The rate of change of money supply could be an indicator of a recession. Let’s see how that compares.

Intervention in the repo market

The federal reserve provides liquidity to the repo market, summary of that action

European central bank

The European central band (ECB) has taken a different path compared to the US Federal Reserve bank.

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Federal Debt

The government is a big driver of the economy, let’s see what it is doing in the debt markets.

datay <- "GFDEBTN"
ylim <- c(0, 28000000)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_Log"
ylim <- c(12, 18)
plotSingleQuick(dfRecession, df.data, datay, ylim)

datay <- "GFDEBTN_YoY"
ylim <- c(-10, 25)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal debt as percent GDP

datay <- "GFDEGDQ188S"
ylim <- c(30, 150)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Federal deficit as percent GDP

datay <- "FYFSGDA188S"
ylim <- c(-30, 5)
plotSingleQuick(dfRecession, df.data, datay, ylim)

Charlie Hatch has a nice format of deficit versus debt:

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Nonfinancial Corporate Business Debt

What about Nonfinancial corporate business and debt securities? Hopefully this doesn’t follow the business loan trends.

That is crazy steep. Time for a log format, see if that brings out the peaks and troughs. That’s a litte better, it looks like there might be a change in slope prior to the recessions.

The derivative doesn’t seem to be much help. There is not much correlation between the zero crossings and the NEBR recessions.

Debt cycle

This analysis roughly follows the ideas in Big Debt Crises book by Ray Dalio.

Total loans

One business cycle theory describes recessions as a market adjustment to mis-allocated assets, often fueled by an credit expansion. That makes the volume of loans an interesting feature to look at. In the presentation of data it looks like the great recession had the largest impact.

Plotting the year over year growth rate helps pull out those small changes in the early years in the data. Peaks can be seen prior to most recessions.

Zoom in to the last couple of decades

As long term interest rates rise, loans should start to tick down. To check this, the total loans and 10 to 1 year spreads are plotted. This is generally the trend observed.

There is a good correlation between these two variables. This next section plots that correction explicitly.

Total loans as percent of GDP

This is the total loans. I think the picture is too broad to point to a specific sector of the economy. The debt burden assumes interest rates are tied to the 10-year treasury: (TOTLNNSA * DGS10) / 100

Commercial and industral loans

Business loans should slow before the recession (a contraction in credit as rates rise).

Commercial and industrial loans as percent of GDP and and income

Look at business debt normalized by GDP over the entire time series. This ratio often peaks at the mid-point of a recession.

https://www.wsj.com/articles/this-isnt-your-fathers-corporate-bond-market-11590574555

“Bonds are behaving more like bank debt, which tends to remain stable or even increase at the onset of recessions, as lenders keep distressed clients afloat—and only later turn off the taps. This was confirmed by a recent report from the Bank for International Settlements. It also found a tight link between this lending cycle and the “real” economy’s booms and busts."

I assume that interest is related to the 10-year treasure: (TOTCINSA * DGS10) / 100

Farm loans

See how the farming sector is fairing.

Real estate loans

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

This gives a big picture, but makes it hard to connect the loans with the income needed to cover those loans. In the next section, loans will be broken up by commercial and residential.

Real Estate (Residential)

In absolute terms the mortgages have increased, but it does not appear to be out of line with the overall economy.

Normalized by GDP it is easier to see the peak in 2008 and that loan levels appear reasonable at the commercial banks.

Maybe the GSE’s are making loans. Take a look at the total mortgages from Z.1 as a percentage of GDP. That does not look too far off trend (ignoring that peak in 2008).

I am assuming that personal income is paying for the mortgages.

Real estate (residential) as percent of GDP and and income

## Warning: Removed 1 rows containing missing values (geom_text).

Consumer loans

Focusing on the consumer sector the growth in debt and incomes can be directly compared. Personal income, as a percent of GDP, remains nearly constant. It is not uncommon for the personal income to rise prior to a recession. Likely this reflect increasing asset prices and market returns. Also interesting to see the loans pick up after interest rates dropped in 1982.

Consumer loans as percent of GDP and and income

Take a closer look since the 2008 recession. Looks like loans are starting to slow as the interest burden rises and incomes remain stable. There are some anomolies in the A065RC1A027NBEA data series because it only updates onces a year. the PI series updates once a month but is noisier and seasonally adjusted. It also shows incomes rising in the middle of the 2008 recession, which doesn’t seem to be accurate.

## Warning: Removed 1 rows containing missing values (geom_text).

## Warning: Removed 1 rows containing missing values (geom_text).

Repo market

This market went through some stress in 2008, it is happening again so setup some plots to watch it.

Nonfincial corporate business security repo asset level

Bonds

T-Bills and Yield Curve

Speaking of loans, interest rates also play into this. This analysis will focus on treasure bills. The 3-month is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

datay <- "TB3MS"
datay.aux <- "DTB3"
ylim <- c(0, 2.5)
dtStart = as.Date('2017-01-01')
p1 <- plotSingle(dfRecession, df.data, "date", datay, getPlotTitle(df.symbols, datay), "Date", 
            getPlotYLabel(df.symbols, datay), c(dtStart, Sys.Date()), ylim, TRUE)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

Check out LIBOR and fed funds rate

The 1-year is plotted below. The yield flattens before a recession as investors go long on bonds and short on equities.

datay <- "DGS10"
datay.aux <- "TNX.Close"
ylim <- c(0, 20)
p1 <- plotSingleQuickModern(datay, ylim)
p1 + geom_line(data=df.data, aes_string(x="date", y=datay.aux, colour=shQuote(datay.aux)), na.rm = TRUE)

Close in, the trend towards inversion be more easily seen. I am also comparing data from the CBOE as well as FRED.

Bond yields are a good proxy for interest rates. As rates rise the theory goes that loans should decrease (inverse correlation).

And a longer window

The yield curve (30 year bond rate minus the 10 year bond rate) may not be a good recession indicator, but a collapse is not good (https://blogs.wsj.com/moneybeat/2018/04/30/theres-more-than-one-part-of-the-yield-curve-getting-flatter/).

The yield curve (10 year bond rate minus the 1 year bond rate) seems to a good indicator of an oncoming recession. It could be a buy indicator by itself.

More recent data

Just the last 24 months or so.

Plot the 10 Year to 3 month over a few decades to see what the outling cases look like

The last two year compare favorably with the period around the 2015-2016 turndown, driven primarily by slowing of the Chinese GDP. Not a debt-driven cycle.

This plot format was suggested by a mises.org article (https://mises.org/wire/yield-curve-accordion-theory), but they only went back to 1988. The date seemed arbitrary so I went back further in time.

Take a look at more recent data

Try looking at a 1-year average of the above time series

High quality bonds

datay <- "AAA"
ylim <- c(2.5, 10)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds to 10-year treasury

High quality bonds long-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('1967-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High quality bonds near-term trend.

datay <- "DGS10ByAAA"
ylim <- c(1, 6.0)
dtStart = as.Date('2007-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

High yield spread

“This data represents the Option-Adjusted Spread (OAS) of the ICE BofAML US Corporate A Index, a subset of the ICE BofAML US Corporate Master Index tracking the performance of US dollar denominated investment grade rated corporate debt publicly issued in the US domestic market. This subset includes all securities with a given investment grade rating A. The ICE BofAML OASs are the calculated spreads between a computed OAS index of all bonds in a given rating category and a spot Treasury curve. An OAS index is constructed using each constituent bond‚Äôs OAS, weighted by market capitalization. When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.”

  • ICE Benchmark Administration Limited (IBA), ICE BofAML US Corporate A Option-Adjusted Spread [BAMLC0A3CA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BAMLC0A3CA, July 4, 2019.
datay <- "BAMLC0A3CA"
ylim <- c(0, 7)
dtStart = as.Date('1997-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Municipal bond market

Suggest by a WSJ article, change in volume for high-risk muni’s. Doesn’t look like there is much too it yet.

https://www.wsj.com/articles/risky-municipal-bonds-are-on-a-hot-streak-11558949401?mod=hp_lead_pos3

datay <- "HYMB.Close"
ylim <- c(40, 62)
dtStart = as.Date('2011-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1 <-
  p1 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

datay <- "HYMB.Volume"
ylim <- c(0, 1750000)
p1.vol <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )

p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p1.vol <-
  p1.vol + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


datay <- "GSPC.Open"
datay_aux <- "GSPC.Close"
ylim <- c(1500, d.GSPC.max )
p2 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2015-08-24"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2016-01-08"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-02-05"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )
p2 <-
  p2 + geom_vline(
    xintercept = as.Date("2018-10-11"),
    linetype = "dashed",
    color = "grey",
    size = 1.0
  )


grid.arrange(p1,
             p1.vol,
             p2,
             ncol = 1,
             top = "High Yield Muni's and S&P Price")

Total Loans and yield curve correlation

This relationship was suggest by Charlie and it is an interesting one. As the yield curve flattens (10-year and 1-year rates converge), total loans grow. The generalization is not always accurate, but it does fit.

## `geom_smooth()` using formula 'y ~ x'

I wanted to see how this looked compared to the 3 month

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 282 rows containing non-finite values (stat_smooth).

Consumer loans and yield curve correlation

Compared to business loans, consumer loans seem to have to response to the 10Y to 3M yield curve.

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 311 rows containing non-finite values (stat_smooth).

Business loans and yield curve correlation

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 104 rows containing non-finite values (stat_smooth).

That’s pretty good correlation. Let’s see what the rolling correlation looks like.

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

datay1 <- "TOTLNNSA_YoY"
ylim1 <- c(-10, 20)

datay2 <- "DGS10TO1"
ylim2 <- c(-5, 10)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 720
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

One other items, let’s see how loans do versus the federal funds rate

## `geom_smooth()` using formula 'y ~ x'

Baker Hughes Rig Count

BEA Supplemental Estimates, Motor Vehicles

Definitions

Autos–all passenger cars, including station wagons.
Light trucks–trucks up to 14,000 pounds gross vehicle weight, including minivans and
sport utility vehicles. Prior to the 2003 Benchmark Revision light trucks were up to 10,000 pounds.
Heavy trucks–trucks more than 14,000 pounds gross vehicle weight.
Prior to the 2003 Benchmark Revision heavy trucks were more than 10,000 pounds.
Domestic sales–United States (U.S.) sales of vehicles assembled in the U.S., Canada, and Mexico.
Foreign sales–U.S. sales of vehicles produced elsewhere.
Domestic auto production–Autos assembled in the U.S.
Domestic auto inventories–U.S. inventories of vehicles assembled in the U.S., Canada, and Mexico.

TAble 6 - Light Vehicle and Total Vehicle Sales

Auto sales

A WSJ article suggested that auto sales might be a good indicator so bring that to the mix. It does have troughs that correlate with recessions

There might be some seasonal variance in the auto sales so lets take a look at the year over year. The data is pretty noisy, it probably will not make a very good indicator.

BEA Gross Domestic Product

Data in this section come from the Bureau of Economic Analysis.

Table 1.1.5. Gross Domestic Product

[Billions of dollars] Seasonally adjusted at annual rates

A191RC: Gross Domestic Product - Line 1

GDP numbers tend to lag so this series is truly an afterthought. But it does have some correlation with the recessions.

GDP does not reflect the capacity of the economy nor the efficiency. Shrinking capacity and lower prices at constant volumes would indicate improvements in effeciency/productivity which is good for the economy, but does not move the GDP upward.

Looks like the year over year change on the GDP should correlate well with unemployment.

Table 1.1.9. Implicit Price Deflators for Gross Domestic Product

[Index numbers, 2012=100] Seasonally adjusted

A191RD: Gross Domestic Product - Line 1

This is GDP price deflator series.

GDP normalized by CPI

Normalize GDP by CPI

Economic yield curve (GDP to 1-year treasury)

GDP versus the yield on the 1-year. This series was prompted by an article suggesting that the “economic yield curve” should be used to indicate a recession rather than an inverted yield curve. Less of indicator and more of concurrent confirmation of recession. Not sure why they would be related either.

Economic yield curve (GDP to 3-month treasury)

Same idea as above, but applied the 3-month treasury.This one has fewer false triggers, but is not as helpful as 10Y to 3M spread in predicting a recession.

A824RC: National defense Federal Gov’t Expenditures - Line 24

U.S. Bureau of Economic Analysis, Federal Government: National Defense Consumption Expenditures and Gross Investment [FDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FDEFX, April 6, 2021.

A825RC: Nondefense Federal Gov’t Expenditures - Line 25

U.S. Bureau of Economic Analysis, Federal Government: Nondefense Consumption Expenditures and Gross Investment [FNDEFX], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/FNDEFX, April 6, 2021.

Table 6.16D. Corporate Profits by Industry

Select series from Table 6.16D

A051RC: Corporate profits with inventory and capital consumption adjustment

From BEA’s documentation (https://www.bea.gov/media/5671):

“BEA’s featured measure of corporate profits — profits from current production - provides a comprehensive and consistent economic measure of the income earned by all U.S. corporations. As such, it is unaffected by changes in tax laws, and it is adjusted for nonreported and misreported income. It excludes dividend income, capital gains and losses, and other financial flows and adjustments, such as deduction for “bad debt.” Thus, the NIPA measure of profits is a particularly useful analytical measure of the health of the corporate sector. For example, in contrast to other popular measures of corporate profits, the NIPA measure did not show the large run-up in profits during the late 1990s that was primarily attributable to capital gains.

Profits after tax with IVA and CCAdj is equal to corporate profits with IVA and CCAdj less taxes on corporate income. It provides an after-tax measure of profits from current production."

Data is Line 1 of Table 6.16D

A053RC: Corporate profits without inventory and capital consumption adjustment

Profits look a bit flat over the last several years in this series.

Table 2.6. Personal Income and Its Disposition, Monthly

Billions of dollars; months are seasonally adjusted at annual rates.

A065RC Personal Income - Line 1

BEA Account Code: A065RC

Personal income is the income that persons receive in return for their provision of labor, land, and capital used in current production and the net current transfer payments that they receive from business and from government.25 Personal income is equal to national income minus corporate profits with inventory valuation and capital consumption adjustments, taxes on production and imports less subsidies, contributions for government social insurance, net interest and miscellaneous payments on assets, business current transfer payments (net), current surplus of government enterprises, and wage accruals less disbursements, plus personal income receipts on assets and personal current transfer receipts. A Guide to the National Income and Product Accounts of the United States (NIPA) - (http://www.bea.gov/national/pdf/nipaguid.pdf)

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Income [PI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PI, July 11, 2019.

DPCERC: Personal consumption expenditures (PCE) - Table 2.1, Line 29

BEA Account Code: DPCERC Personal consumption expenditures (PCE) is the primary measure of consumer spending on goods and services in the U.S. economy. 1 It accounts for about two-thirds of domestic final spending, and thus it is the primary engine that drives future economic growth. PCE shows how much of the income earned by households is being spent on current consumption as opposed to how much is being saved for future consumption. -https://www.bea.gov/system/files/2019-12/Chapter-5.pdf

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures [PCE], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCE, June 12, 2020

DPCERG: Personal consumption expenditures Price Index (PCEPI) - Table 2.1, Line 29

BEA Account Code: DPCERG The gross domestic product price index measures changes in prices paid for goods and services produced in the United States, including those exported to other countries. Prices of imports are excluded. The gross domestic product implicit price deflator, or GDP deflator, basically measures the same things and closely mirrors the GDP price index, although the two price measures are calculated differently. The GDP deflator is used by some firms to adjust payments in contracts.

The gross domestic purchases price index is BEA’s featured measure of inflation for the U.S. economy overall. It measures changes in prices paid by consumers, businesses, and governments in the United States, including the prices of the imports they buy.

BEA’s closely followed personal consumption expenditures price index, or PCE price index, is a narrower measure. It looks at the changing prices of goods and services purchased by consumers in the United States. It’s similar to the Bureau of Labor Statistics’ consumer price index for urban consumers. The two indexes, which have their own purposes and uses, are constructed differently, resulting in different inflation rates.

The PCE price index is known for capturing inflation (or deflation) across a wide range of consumer expenses and for reflecting changes in consumer behavior. For example, if the price of beef rises, shoppers may buy less beef and more chicken. Also, BEA revises previously published PCE data to reflect updated information or new methodology, providing consistency across decades of data that’s valuable for researchers. The PCE price index is used primarily for macroeconomic analysis and forecasting. -https://www.bea.gov/resources/learning-center/what-to-know-prices-inflation

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Consumption Expenditures: Chain-type Price Index [PCEPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PCEPI, April 25, 2021.

A072RC: Personal Savings Rate - Line 35

Consumers tend to pull down their savings rates as unemployment decreases and market conditions improve. This series has tended to be unreliable due to the size of revisions during the comprehensive update carried out by the BEA. The last update on this series moved the rate from 4.2 to 6.7 percent.

(https://www.bloomberg.com/news/articles/2018-07-27/americans-have-been-saving-much-more-than-thought-new-data-show)

BEA Account Code: A072RC Personal saving as a percentage of disposable personal income (DPI), frequently referred to as “the personal saving rate,” is calculated as the ratio of personal saving to DPI. Personal saving is equal to personal income less personal outlays and personal taxes; it may generally be viewed as the portion of personal income that is used either to provide funds to capital markets or to invest in real assets such as residences.(https://www.bea.gov/national/pdf/all-chapters.pdf) A Guide to the National Income and Product Accounts of the United States (NIPA).

Suggested Citation: U.S. Bureau of Economic Analysis, Personal Saving Rate [PSAVERT], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/PSAVERT, July 9, 2019.

Take a closer look at the last decade

The relationship between personal savings and unemployment (U-3) can be better visualized with a scatter plot

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 190 rows containing non-finite values (stat_smooth).

The fit does not explain most of what is in the plot. Lets take a look at the rolling correlation.

datay1 <- "UNRATE"
ylim1 <- c(2, 12)

datay2 <- "PSAVERT"
ylim2 <- c(0, 35)

dtStart <- as.Date("1jan1985","%d%b%Y")

w <- 360
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Personal savings to household net worth

A relationship between personal savings and household networth can be seen in a scatter plot. This was suggested by a WSJ article (https://blogs.wsj.com/dailyshot/2018/02/23/the-daily-shot-reasons-for-declining-u-s-household-savings-rate/).

## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 499 rows containing non-finite values (stat_smooth).

U.S. Census Bureau

U.S. International Trade in Goods and Services (FT900)

U.S. Bureau of Economic Analysis and U.S. Census Bureau, U.S. Imports of Goods by Customs Basis from China [IMPCH], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/IMPCH, October 5, 2019.

New Houses Sold and For Sale by Stage of Construction and Median Number of Months on Sales Market

Read an article suggesting that housing sales and sales growth could be useful. FRED only has new home data so start there.

datay <- "HSN1FNSA"
ylim <- c(0, 200)
dtStart = as.Date('1964-01-01')
p1 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA"
ylim <- c(0, 600)
p2 <- plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "HNFSUSNSA.minus.HSN1FNSA"
ylim <- c(0, 600)
p3 <-
  plotSingle(
    dfRecession,
    df.data,
    "date",
    datay,
    getPlotTitle(df.symbols, datay),
    "Date",
    getPlotYLabel(df.symbols, datay),
    c(dtStart, Sys.Date()),
    ylim,
    TRUE
  )

grid.arrange(p1,
             p2,
             p3,
             ncol = 1,
             top = "New Housing Sales")

New housing yoy

New Privately-Owned Housing Units Authorized in Permit-Issuing Places

As provided by the Census, start occurs when excavation begins for the footings or foundation of a building. All housing units in a multifamily building are defined as being started when this excavation begins. Beginning with data for September 1992, estimates of housing starts include units in structures being totally rebuilt on an existing foundation.

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Housing Starts: Total: New Privately Owned Housing Units Started [HOUST], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/HOUST, June 13, 2020.

Take a look at privately owned starts

New Privately-Owned Houses Sold and For Sale

Suggested Citation: U.S. Census Bureau and U.S. Department of Housing and Urban Development, Median Sales Price of Houses Sold for the United States [MSPUS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/MSPUS, June 13, 2020.

Finally, take a look at starts times the median price

Durable Goods

Suggested Citation: U.S. Census Bureau, Manufacturers’ New Orders: Durable Goods [UMDMNO], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/UMDMNO, April 26, 2021.

Durable goods, not seasonally adjusted, divided by GDP

Durable goods, seasonally adjusted, divided by GDP

Federal reserve board H.8: Assets and Liabilities of Commercial Banks in the United States

Page 4: Not Seasonally adjusted, billions of dollars

Commercial and industrial loans, all commercial banks - Line 10

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Commercial and Industrial Loans, All Commercial Banks [BUSLOANS], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/BUSLOANS, July 11, 2019.

Taking a look at the difference in SA and NSA series. Seasonal adjustments do vary, but do not seem to be related to recessions.

The raw series is just too steep for any kind of machine learnine. This needs to be converted to log scale.

That’s a little better, let’s see what the smoothed derivative looks like.

That is odd…looks like this doesn’t cross zero unless we are getting close to, or into, a recession. The year over year tells about the same story. Might be a good indication of the end of a recession.

Consumer loans, all commercial banks - Line 20

Suggested Citation: Board of Governors of the Federal Reserve System (US), Consumer Loans, All Commercial Banks [CONSUMERNSA], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CONSUMERNSA, July 11, 2019.

That spike in consumer loans is due to

“April 9, 2010 (Last revised September 23, 2011): As of the week ending March 31, 2010, domestically chartered banks and foreign-related institutions had consolidated onto their balance sheets the following assets and liabilities of off-balance-sheet vehicles, owing to the adoption of FASB’s Financial Accounting Statements No. 166 (FAS 166),”Accounting for Transfers of Financial Assets," and No. 167 (FAS 167), “Amendments to FASB Interpretation No. 46(R).”

This included a consumer loans, credit cards and other revolving plans change of $321.9B. That was a lot of off-balance-sheet bank assets.

Deposits, All Commercial Banks, all commercial banks - Line 34

Data taken from H.8 Assets and Liabilities of Commercial Banks in the United States. Take a look at SA and NSA data series as weekly and month updates. It should all be similar at this scale.

Suggested Citation: Board of Governors of the Federal Reserve System (US), Deposits, All Commercial Banks [DPSACBW027SBOG], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/DPSACBW027SBOG, May 14, 2020.

Federal reserve board Z.1: Financial Accounts of the United States

From the FRED website (https://fred.stlouisfed.org/release?rid=52):

"The Financial Accounts (formerly known as the Flow of Funds accounts) are a set of financial accounts used to track the sources and uses of funds by sector. They are a component of a system of macroeconomic accounts including the National Income and Product accounts (NIPA) and balance of payments accounts, all of which serve as a comprehensive set of information on the economy’s performance.(1) Some important inferences that can be drawn from the Financial accounts are the financial strength of a given sector, new economic trends, changes in the composition of wealth, and development of new financial instruments over time.(1)

Sectors are compiled into three categories: households, nonfinancial businesses, and banks. The sources of funds for a sector are its internal funds (savings from income after consumption) and external funds (loans from banks and other financial intermediaries). (1) Funds for a given sector are used for its investments in physical and financial assets. Dividing sources and uses of funds into two categories helps the staff of the Federal Reserve System pay particular attention to external sources of funds and financial uses of funds.(2) One example is whether households are borrowing more from banks—or in other words, whether household debt is rising. Another example might be whether banks are using more of their funds to provide loans to consumers. Transactions within a sector are not shown in the accounts; however, transactions between sectors are.(2) Monitoring the external flows of funds provides insights into a sector’s health and the performance of the economy as a whole.

Data for the Financial accounts are compiled from a large number of reports and publications, including regulatory reports such as those submitted by banks, tax filings, and surveys conducted by the Federal Reserve System.(2) The Financial accounts are published quarterly as a set of tables in the Federal Reserve’s Z.1 statistical release.

  1. Teplin, Albert M. “The U.S. Flow of Funds Accounts and Their Uses.” Federal Reserve Bulletin, July 2001; http://www.federalreserve.gov/pubs/bulletin/2001/0701lead.pdf.
  2. Board of Governors of the Federal Reserve System. “Guide to the Flow of Funds Accounts.” 2000, http://www.federalreserve.gov/apps/fof/."

L.102 Nonfinancial Business

FL102051003.Q: Nonfinancial corporate business; security repurchase agreements; asset

Asset level of nonfinancial business security repo agreements. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL102051003&t=

L.214 Loans

FL894123005.Q: All sectors; total loans; liability

Sum of domestic financial sectors, all sectors, total mortgages, and households/non-profits. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL894123005&t=L.107&bc=L.107:FL793068005&suf=Q

FL793068005.Q: Domestic financial sectors; depository institution loans n.e.c.; asset

Sum of Monetary authority; depository institution loans n.e.c.; asset and Private depository institutions; depository institution loans n.e.c.; asset. federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL793068005&t=L.214&suf=Q

FL893169005.Q: All sectors; other loans and advances; liability

Sum of finance, government, and chartered institutions asset levels. https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893169005&t=L.214&suf=Q

FL893065105.Q: All sectors; home mortgages; asset

https://www.federalreserve.gov/apps/fof/DisplayTable.aspx?t=L.214

FL893065405.Q: All sectors; multifamily residential mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065405&t=L.214&suf=Q

FL893065505.Q: All sectors; commercial mortgages; asset

https://www.federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL893065505&t=L.214&suf=Q

FL153166000.Q: Households and nonprofit organizations; consumer credit; liability

federalreserve.gov/apps/fof/SeriesAnalyzer.aspx?s=FL153166000&t=L.214&suf=Q

B.101 Balance Sheet of Households and Nonprofit Organizations

FL152000005.Q: Households and nonprofit organizations; total assets, Level

string.source ID: FL152000005.Q.

FL152090006.Q: Household Net Worth as Percentage of Disposable Personal Income

string.source ID: FL152090006.Q. Household networth tends to fall as a recession start.

Productivity Yield Curve

GDP versus productivity

Manufacturing output and employees

Not sure if these relates to a recession, but fascinating to see how output and employees change with time.

datay <- "OUTMS"
ylim <- c(60, 120)
dtStart = as.Date('1987-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "MANEMP"
ylim <- c(10000, 20000)
dtStart = as.Date('1948-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "PRS30006163"
ylim <- c(40, 120)
dtStart = as.Date('1986-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Shipping volumes might be helpful in determining state of the economy.

datay <- "FRGSHPUSM649NCIS"
ylim <- c(0.8, 1.4)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

datay <- "FRGSHPUSM649NCIS_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Freight, loosely, moves inversely to the trade deficit.

datay <- "BOPGTB_YoY"
ylim <- c(-30, 30)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

World bank air transportation. Only updated annually so less usefull, but interesting reference to above.

datay <- "WWDIWLDISAIRGOODMTK1"
ylim <- c(0, 250000)
dtStart = as.Date('1999-01-01')
plotSingleQuick(dfRecession, df.data, datay, ylim, dtStart)

Gross private domestic investment

Spending most certainly tips down prior to a recession. The gross private domestic investment data series, plotted in log format below, show how private investment pulls back prior to recessions.

The change in direction is a little easier to see if the derivative is plotted, first YoY then the smoothed derivative

Velocity

Productivity

Date range to match census data

PMI

Industrial Production

This is a look at manufacturing industrial production. The yoY change should be a leading indicator of unemployment.

Housing

Take a look at housing starts. These can drop as rates rise.

Case-schiller price index

Population data

Many of the economic series can be better understood if normalized by population. Basic population and worker data from FRED.

Population to GDP

Look at GDP divided by CPI per person. It flattens and even dips a little prior to a recession. Might be worth looking at the derivative of this series.

That is worth a closer look

datay1 <- "GDPBYCPIAUCSLBYPOPTHM_SmoothDer"
ylim1 <- c(-5, 5)

datay2 <- "RecInit_Smooth"
ylim2 <- c(0, 1)

dtStart <- as.Date("1jan1960","%d%b%Y")

w <- 30
corrName <- calcRollingCorr(dfRecession, df.data, df.symbols, datay1, ylim1, datay2, ylim2, w, dtStart)

Correlation Study

Detailed correlations are explored above. Before concluding, let’s take a look at some overall correlation values to see if anything pops out.

Commodities

As mentioned above, copper, year over year, has some correlation with the recession initiation. It could be useful.

GDP Series

GDP, normalized first by CPI and then by population, looks like it migh correlate inversely with the recession indicators

Financials

Let’s see where we are so far. The correlation plot confirms some of the speculation above. The S&P 500 (GSPC.Open) is well correlated with industrial production (INDPRO), business loans (BUSLOANS), total loans (TOTLNNSA) , and nonfinancial corporate business debt (NCBDBIQ027S).

In this case, I want and indicator that rises prior to a recession. It looks like the unemployment rate (UNRATE), real personal income (W875RX1), and the yield curve (DGS10TO1) are all inversely correlated with the recession initiation indicator.

I thought the modified recession initiation would be a harder match, but there are quite a few correlated variables. Lets take a look at some of those in more detail

Complete list of symbols

Since it is tedious to do this one at a time, all the symbols were entered into a data frame, loaded, and aggregated together in a single xts object.

This is the complete list of symbol names and sources used in the project.